% Encoding: UTF-8

@InProceedings{wen2016centerloss,
  author    = {Wen, Y. and Zhang, K. and Li, Z. and Qiao, Y.},
  booktitle = {ECCV (7)},
  title     = {A Discriminative Feature Learning Approach for Deep Face Recognition.},
  year      = {2016},
  editor    = {Leibe, Bastian and Matas, Jiri and Sebe, Nicu and Welling, Max},
  pages     = {499-515},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {9911},
  added-at  = {2017-05-19T00:00:00.000+0200},
  biburl    = {https://www.bibsonomy.org/bibtex/21f54c145e212f97f6a7d027346842585/dblp},
  ee        = {https://doi.org/10.1007/978-3-319-46478-7_31},
  interhash = {d0ff82b6a72254876fdcb6bbf7cc46fe},
  intrahash = {1f54c145e212f97f6a7d027346842585},
  isbn      = {978-3-319-46477-0},
  keywords  = {dblp},
  timestamp = {2019-05-15T18:28:57.000+0200},
  url       = {http://dblp.uni-trier.de/db/conf/eccv/eccv2016-7.html#WenZL016},
}
@article{Tolstikhin2018WassersteinA,
  title={Wasserstein Auto-Encoders},
  author={Tolstikhin, I. O. and  Bousquet, O. and Gelly, S. and Sch{\"o}lkopf, B.},
  journal={CoRR},
  year={2018},
  volume={abs/1711.01558}
}

@article{radford2015unsupervised,
  title={Unsupervised representation learning with deep convolutional generative adversarial networks},
  author={Radford, Alec and Metz, Luke and Chintala, Soumith},
  journal={arXiv preprint arXiv:1511.06434},
  year={2015}
}

@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1--9},
  year={2015}
}

@article{russakovsky2015imagenet,
  title={Imagenet large scale visual recognition challenge},
  author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others},
  journal={International Journal of Computer Vision},
  volume={115},
  number={3},
  pages={211--252},
  year={2015},
  publisher={Springer}
}

@article{metz2016unrolled,
  title={Unrolled generative adversarial networks},
  author={Metz, Luke and Poole, Ben and Pfau, David and Sohl-Dickstein, Jascha},
  journal={arXiv preprint arXiv:1611.02163},
  year={2016}
}
{
hoang2018mgan,
title={{MGAN}: Training Generative Adversarial Nets with Multiple Generators},
author={Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung},
journal={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rkmu5b0a-},
}

@Article{krizhevsky2009learning,
  author    = {Krizhevsky, A. and Hinton, G.},
  title     = {Learning multiple layers of features from tiny images},
  year      = {2009},
  publisher = {Technical report, University of Toronto},
}

@inproceedings{heusel2017gans,
  title={Gans trained by a two time-scale update rule converge to a local nash equilibrium},
  author={Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
  booktitle={Advances in Neural Information Processing Systems},
  pages={6629--6640},
  year={2017}
}


@article{dowson1982frechet,
  title={The Fr{\'e}chet distance between multivariate normal distributions},
  author={Dowson, DC and Landau, BV},
  journal={Journal of multivariate analysis},
  volume={12},
  number={3},
  pages={450--455},
  year={1982},
  publisher={Elsevier}
}

@inproceedings{salimans2016improved,
  title={Improved techniques for training gans},
  author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2234--2242},
  year={2016}
}

@inproceedings{gulrajani2017improved,
  title={Improved training of wasserstein gans},
  author={Gulrajani, Ishaan and Ahmed, Faruk and Arjovsky, Martin and Dumoulin, Vincent and Courville, Aaron C},
  booktitle={Advances in Neural Information Processing Systems},
  pages={5769--5779},
  year={2017}
}

@article{ba2016layer,
  title={Layer normalization},
  author={Ba, Jimmy Lei and Kiros, Jamie Ryan and Hinton, Geoffrey E},
  journal={arXiv preprint arXiv:1607.06450},
  year={2016}
}

@article{miyato2018spectral,
title={Spectral Normalization for Generative Adversarial Networks},
author={Miyato, Takeru and Kataoka, Toshiki and Koyama, Masanori and Yoshida, Yuichi},
journal={International Conference on Learning Representations},
year={2018},
}

@inproceedings{liu2015deep,
  title={Deep learning face attributes in the wild},
  author={Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={3730--3738},
  year={2015}
}

@Article{arjovsky_etal_arxiv17_wasserstein_gan,
  author  = {Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'e}on},
  title   = {Wasserstein GAN},
  journal = {arXiv preprint arXiv:1701.07875},
  year    = {2017},
  file    = {:../deep_learning/Wasserstein GANs.pdf:PDF},
  groups  = {GANs},
}

@Article{berthelot2017began,
  author  = {Berthelot, David and Schumm, Tom and Metz, Luke},
  title   = {BEGAN: Boundary Equilibrium Generative Adversarial Networks},
  journal = {arXiv preprint arXiv:1703.10717},
  year    = {2017},
}

@Article{wang2017magan,
  author  = {Wang, Ruohan and Cully, Antoine and Chang, Hyung Jin and Demiris, Yiannis},
  title   = {MAGAN: Margin Adaptation for Generative Adversarial Networks},
  journal = {arXiv preprint arXiv:1704.03817},
  year    = {2017},
  file    = {:../deep_learning/MAGAN - Margin Adaptation for Generative Adversarial Networks.pdf:PDF},
  groups  = {GANs},
}


@Article{arora2017generalization,
  author  = {Arora, Sanjeev and Ge, Rong and Liang, Yingyu and Ma, Tengyu and Zhang, Yi},
  title   = {Generalization and Equilibrium in Generative Adversarial Nets (GANs)},
  journal = {arXiv preprint arXiv:1703.00573},
  year    = {2017},
  groups  = {GANs},
}


@Article{dumoulin2016adversarially,
  author  = {Dumoulin, Vincent and Belghazi, Ishmael and Poole, Ben and Lamb, Alex and Arjovsky, Martin and Mastropietro, Olivier and Courville, Aaron},
  title   = {Adversarially learned inference},
  journal = {arXiv preprint arXiv:1606.00704},
  year    = {2016},
}

@inproceedings{ioffe2015batch,
  title={Batch normalization: Accelerating deep network training by reducing internal covariate shift},
  author={Ioffe, Sergey and Szegedy, Christian},
  booktitle={International Conference on Machine Learning},
  pages={448--456},
  year={2015}
}

@InProceedings{coates2011analysis,
  author    = {Coates, A. and Ng, A. and Lee, H.},
  title     = {An analysis of single-layer networks in unsupervised feature learning},
  booktitle = {Proceedings of the fourteenth international conference on artificial intelligence and statistics},
  year      = {2011},
  pages     = {215--223},
}

@article{theis2015note,
  title={A note on the evaluation of generative models},
  author={Theis, Lucas and Oord, A{\"a}ron van den and Bethge, Matthias},
  journal={arXiv preprint arXiv:1511.01844},
  year={2015}
}

@article{huszar2015not,
  title={How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?},
  author={Husz{\'a}r, Ferenc},
  journal={arXiv preprint arXiv:1511.05101},
  year={2015}
}


@article{yu2015lsun,
  title={Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop},
  author={Yu, Fisher and Seff, Ari and Zhang, Yinda and Song, Shuran and Funkhouser, Thomas and Xiao, Jianxiong},
  journal={arXiv preprint arXiv:1506.03365},
  year={2015}
}

@InProceedings{goodfellow2014generative,
  author    = {Goodfellow, I. and Pouget-Abadie, J. and Mirza, M. and Xu, B. and Warde-Farley, D. and Ozair, S. and Courville, A. and Bengio, Y.},
  booktitle = {NIPS},
  title     = {Generative adversarial nets},
  year      = {2014},
}

@article{goodfellow2016nips,
  title={NIPS 2016 tutorial: Generative adversarial networks},
  author={Goodfellow, Ian},
  journal={arXiv preprint arXiv:1701.00160},
  year={2016}
}

@article{zhao2016energy,
  title={Energy-based generative adversarial network},
  author={Zhao, Junbo and Mathieu, Michael and LeCun, Yann},
  journal={arXiv preprint arXiv:1609.03126},
  year={2016}
}

@Article{Huszar2015,
  author  = {Husz{\'a}r, Ferenc},
  title   = {How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?},
  journal = {arXiv preprint arXiv:1511.05101},
  year    = {2015},
}

@Article{Theis2015,
  author  = {Theis, Lucas and Oord, A{\"a}ron van den and Bethge, Matthias},
  title   = {A note on the evaluation of generative models},
  journal = {arXiv preprint arXiv:1511.01844},
  year    = {2015},
}

@article{warde2016improving,
  title={Improving generative adversarial networks with denoising feature matching},
  author={Warde-Farley, David and Bengio, Yoshua},
  year={2016}
}

@article{kingma2013auto,
  title={Auto-encoding variational bayes},
  author={Kingma, Diederik P and Welling, Max},
  journal={arXiv preprint arXiv:1312.6114},
  year={2013}
}

@article{larsen2015autoencoding,
  title={Autoencoding beyond pixels using a learned similarity metric},
  author={Larsen, Anders Boesen Lindbo and S{\o}nderby, S{\o}ren Kaae and Larochelle, Hugo and Winther, Ole},
  journal={arXiv preprint arXiv:1512.09300},
  year={2015}
}

@article{bao2017cvae,
  title={CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training},
  author={Bao, Jianmin and Chen, Dong and Wen, Fang and Li, Houqiang and Hua, Gang},
  journal={arXiv preprint arXiv:1703.10155},
  year={2017}
}

@article{alain2014regularized,
  title={What regularized auto-encoders learn from the data-generating distribution},
  author={Alain, Guillaume and Bengio, Yoshua},
  journal={The Journal of Machine Learning Research},
  volume={15},
  number={1},
  pages={3563--3593},
  year={2014},
  publisher={JMLR. org}
}

@InProceedings{krizhevsky2012imagenet,
  author    = {Krizhevsky, A. and Sutskever, I. and Hinton, G. E},
  booktitle = {NIPS},
  title     = {Imagenet classification with deep convolutional neural networks},
  year      = {2012},
}
@InProceedings{tu_etal_nips17_d2gan,
  author    = {Tu Dinh Nguyen and Trung Le and Hung Vu and Dinh Phung},
  title     = {Dual Discriminator Generative Adversarial Nets},
  booktitle = {Advances in Neural Information Processing Systems 29 (NIPS)},
  year      = {2017}
}

@article{odena2016conditional,
  title={Conditional image synthesis with auxiliary classifier gans},
  author={Odena, Augustus and Olah, Christopher and Shlens, Jonathon},
  journal={arXiv preprint arXiv:1610.09585},
  year={2016}
}

@article{zhang2016stackgan,
  title={Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks},
  author={Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Huang, Xiaolei and Wang, Xiaogang and Metaxas, Dimitris},
  journal={arXiv preprint arXiv:1612.03242},
  year={2016}
}

@article{makhzani2015adversarial,
  title={Adversarial autoencoders},
  author={Makhzani, Alireza and Shlens, Jonathon and Jaitly, Navdeep and Goodfellow, Ian and Frey, Brendan},
  journal={arXiv preprint arXiv:1511.05644},
  year={2015}
}

@article{mescheder2017adversarial,
  title={Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks},
  author={Mescheder, Lars and Nowozin, Sebastian and Geiger, Andreas},
  journal={arXiv preprint arXiv:1701.04722},
  year={2017}
}

@Article{Larsen2015,
  author  = {Larsen, Anders Boesen Lindbo and S{\o}nderby, S{\o}ren Kaae and Larochelle, Hugo and Winther, Ole},
  title   = {Autoencoding beyond pixels using a learned similarity metric},
  journal = {arXiv preprint arXiv:1512.09300},
  year    = {2015},
}

@article{ulyanov2017adversarial,
  title={Adversarial Generator-Encoder Networks},
  author={Ulyanov, Dmitry and Vedaldi, Andrea and Lempitsky, Victor},
  journal={arXiv preprint arXiv:1704.02304},
  year={2017}
}

@Article{Yu2015,
  author  = {Yu, Fisher and Seff, Ari and Zhang, Yinda and Song, Shuran and Funkhouser, Thomas and Xiao, Jianxiong},
  title   = {Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop},
  journal = {arXiv preprint arXiv:1506.03365},
  year    = {2015},
}


@article{tolstikhin2017adagan,
  title={Adagan: Boosting generative models},
  author={Tolstikhin, Ilya and Gelly, Sylvain and Bousquet, Olivier and Simon-Gabriel, Carl-Johann and Sch{\"o}lkopf, Bernhard},
  journal={arXiv preprint arXiv:1701.02386},
  year={2017}
}

@article{neumann1928theorie,
  title={Zur theorie der gesellschaftsspiele},
  author={Neumann, J v},
  journal={Mathematische annalen},
  volume={100},
  number={1},
  pages={295--320},
  year={1928},
  publisher={Springer}
}

@article{wang2016ensembles,
  title={Ensembles of generative adversarial networks},
  author={Wang, Yaxing and Zhang, Lichao and van de Weijer, Joost},
  journal={arXiv preprint arXiv:1612.00991},
  year={2016}
}

@article{ghosh2017multi,
  title={Multi-Agent Diverse Generative Adversarial Networks},
  author={Ghosh, Arnab and Kulharia, Viveka and Namboodiri, Vinay and Torr, Philip HS and Dokania, Puneet K},
  journal={arXiv preprint arXiv:1704.02906},
  year={2017}
}

@article{durugkar2016generative,
  title={Generative Multi-Adversarial Networks},
  author={Durugkar, Ishan and Gemp, Ian and Mahadevan, Sridhar},
  journal={arXiv preprint arXiv:1611.01673},
  year={2016}
}

@inproceedings{maas2013rectifier,
  title={Rectifier nonlinearities improve neural network acoustic models},
  author={Maas, Andrew L and Hannun, Awni Y and Ng, Andrew Y},
  booktitle={Proc. ICML},
  volume={30},
  number={1},
  year={2013}
}

@inproceedings{nair2010rectified,
  title={Rectified linear units improve restricted boltzmann machines},
  author={Nair, Vinod and Hinton, Geoffrey E},
  booktitle={Proceedings of the 27th international conference on machine learning (ICML-10)},
  pages={807--814},
  year={2010}
}

@article{goodfellow2013maxout,
  title={Maxout networks},
  author={Goodfellow, Ian J and Warde-Farley, David and Mirza, Mehdi and Courville, Aaron and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1302.4389},
  year={2013}
}

@article{klambauer2017self,
  title={Self-Normalizing Neural Networks},
  author={Klambauer, G{\"u}nter and Unterthiner, Thomas and Mayr, Andreas and Hochreiter, Sepp},
  journal={arXiv preprint arXiv:1706.02515},
  year={2017}
}

@Article{abadi2016tensorflow,
  author  = {Abadi, M. and Agarwal, A. and Barham, P. and Brevdo, E. and Chen, Z. and Citro, C. and Corrado, G. S. and Davis, A. and Dean, J. and Devin, M. and others},
  title   = {Tensorflow: Large-scale machine learning on heterogeneous distributed systems},
  journal = {arXiv preprint arXiv:1603.04467},
  year    = {2016},
}

% GAN apps
@article{mathieu2015deep,
  title={Deep multi-scale video prediction beyond mean square error},
  author={Mathieu, Michael and Couprie, Camille and LeCun, Yann},
  journal={arXiv preprint arXiv:1511.05440},
  year={2015}
}

@inproceedings{zhang2017stackgan,
  title={Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks},
  author={Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Huang, Xiaolei and Wang, Xiaogang and Metaxas, Dimitris},
  booktitle={IEEE Int. Conf. Comput. Vision (ICCV)},
  pages={5907--5915},
  year={2017}
}

@article{isola2017image,
  title={Image-to-image translation with conditional adversarial networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  journal={arXiv preprint},
  year={2017}
}

@inproceedings{wu2016learning,
  title={Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling},
  author={Wu, Jiajun and Zhang, Chengkai and Xue, Tianfan and Freeman, Bill and Tenenbaum, Josh},
  booktitle={Advances in Neural Information Processing Systems},
  pages={82--90},
  year={2016}
}

@article{ledig2016photo,
  title={Photo-realistic single image super-resolution using a generative adversarial network},
  author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others},
  journal={arXiv preprint},
  year={2016}
}

@inproceedings{nowozin2016f,
  title={f-gan: Training generative neural samplers using variational divergence minimization},
  author={Nowozin, Sebastian and Cseke, Botond and Tomioka, Ryota},
  booktitle={Advances in Neural Information Processing Systems},
  pages={271--279},
  year={2016}
}

@article{hoang2017multi,
  title={Multi-Generator Gernerative Adversarial Nets},
  author={Hoang, Quan and Nguyen, Tu Dinh and Le, Trung and Phung, Dinh},
  journal={arXiv preprint arXiv:1708.02556},
  year={2017}
}

@article{arjovsky2017wasserstein,
  title={Wasserstein gan},
  author={Arjovsky, Martin and Chintala, Soumith and Bottou, L{\'e}on},
  journal={arXiv preprint arXiv:1701.07875},
  year={2017}
}

@article{mirza2014conditional,
  title={Conditional generative adversarial nets},
  author={Mirza, Mehdi and Osindero, Simon},
  journal={arXiv preprint arXiv:1411.1784},
  year={2014}
}

@inproceedings{chen2016infogan,
  title={Infogan: Interpretable representation learning by information maximizing generative adversarial nets},
  author={Chen, X. and Duan, Y. and Houthooft, R. and Schulman, J. and Sutskever, I. and Abbeel, P.},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2172--2180},
  year={2016}
}

@article{nguyen2010estimating,
  title={Estimating divergence functionals and the likelihood ratio by convex risk minimization},
  author={Nguyen, X. L. and Wainwright, M. J. and Jordan, M. I.},
  journal={IEEE Transactions on Information Theory},
  volume={56},
  number={11},
  pages={5847--5861},
  year={2010},
  publisher={IEEE}
}

@book{vapnik99,
  author = {Vapnik, V. N.},
  edition = {Second},
  howpublished = {Hardcover},
  isbn = {0387987800},
  keywords = {kernel-method, learning-theory, svm},
  month = {November},
  owner = {sonntag},
  priority = {5},
  publisher = {Springer},
  title = {The Nature of Statistical Learning Theory},
  year = {1999}
}

@article{Borgwardt2006,
 author = {Borgwardt, K. M. and Gretton, A. and Rasch, M. J. and Kriegel, H.-P. and Sch\"{o}lkopf, B. and Smola, A. J.},
 title = {Integrating Structured Biological Data by Kernel Maximum Mean Discrepancy},
 journal = {Bioinformatics},
 issue_date = {July 2006},
 volume = {22},
 number = {14},
 month = jul,
 year = {2006},
 issn = {1367-4803},
 pages = {e49--e57},
 publisher = {Oxford University Press},
 address = {Oxford, UK}
} 

@incollection{Huang2006,
title = {Correcting Sample Selection Bias by Unlabeled Data},
author = {Jiayuan Huang and Gretton, Arthur and Karsten M. Borgwardt and Sch\"{o}lkopf, Bernhard and Alex J. Smola},
booktitle = {Advances in Neural Information Processing Systems 19},
editor = {Sch\"{o}lkopf, B. and  Platt, J. C. and  Hoffman, T.},
pages = {601--608},
year = {2007},
publisher = {MIT Press}
}

@InProceedings{Gong2013,
  author    = {Gong, B. and Grauman, K. and Sha, F.},
  booktitle = {ICML},
  title     = {Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation},
  year      = {2013},
}

@InProceedings{Pan2009,
  author    = {Pan, S. J. and Tsang, I. W. and Kwok, J. T. and Yang, Q.},
  booktitle = {IJCAI},
  title     = {Domain Adaptation via Transfer Component Analysis},
  year      = {2009},
}

@inproceedings{Gopalan2011,
 author = {Gopalan, R. and Li, R. and Chellappa, R.},
 title = {Domain Adaptation for Object Recognition: An Unsupervised Approach},
 booktitle = {Proceedings of the 2011 International Conference on Computer Vision},
 series = {ICCV '11},
 year = {2011},
 isbn = {978-1-4577-1101-5},
 pages = {999--1006},
 numpages = {8},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
} 

@INPROCEEDINGS{Baktashmotlagh2013, 
author={Baktashmotlagh, M. and  Harandi, M. T. and  Lovell, B. C. and Salzmann, M.}, 
booktitle={2013 IEEE International Conference on Computer Vision}, 
title={Unsupervised Domain Adaptation by Domain Invariant Projection}, 
year={2013}, 
volume={}, 
number={}, 
pages={769-776}, 
ISSN={1550-5499}, 
month={Dec},}

@InProceedings{Ganin2015,
  author    = {Ganin, Y. and Lempitsky, V.},
  booktitle = {ICML},
  title     = {Unsupervised Domain Adaptation by Backpropagation},
  year      = {2015},
}

@InProceedings{long2015,
  author    = {Long, M. and Cao, Y. and Wang, J. and Jordan, M. I.},
  booktitle = {ICML},
  title     = {Learning Transferable Features with Deep Adaptation Networks},
  year      = {2015},
}

@InProceedings{shu2018a,
  author    = {Shu, R. and Bui, H. H. and Narui, H. and Ermon, S.},
  booktitle = {ICLR},
  title     = {A {DIRT}-T Approach to Unsupervised Domain Adaptation},
  year      = {2018},
}

@InProceedings{french2017self,
  author    = {{et al.}, G. French},
  booktitle = {ICLR},
  title     = {Self-ensembling for visual domain adaptation},
  year      = {2018},
}

@Article{courty2017optimal,
  author  = {Courty, N. and Flamary, R. and Tuia, D. and Rakotomamonjy, A.},
  journal = {IEEE TPAMI},
  title   = {Optimal transport for domain adaptation},
  year    = {2017},
}

@InProceedings{gretton2007kernel,
  author    = {Gretton, A. and Borgwardt, K. M. and Rasch, M. and Sch{\"o}lkopf, B. and Smola, A. J.},
  booktitle = {NIPS},
  title     = {A kernel method for the two-sample-problem},
  year      = {2007},
}

@Article{bendavid2010theoryda,
  author  = {Ben-David, S. and Blitzer, J. and Crammer, K. and Kulesza, A. and Pereira, F. and Vaughan, J. W.},
  journal = {Mach. Learn.},
  title   = {A Theory of Learning from Different Domains},
  year    = {2010},
}

@InCollection{Mansour2009,
  author    = {Mansour, Y. and Mohri, M. and Rostamizadeh, A.},
  booktitle = {NIPS},
  title     = {Domain Adaptation with Multiple Sources},
  year      = {2009},
}

@inproceedings{redko2017theoretical,
  title={Theoretical analysis of domain adaptation with optimal transport},
  author={Redko, I. and Habrard, A. and Sebban, M.},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={737--753},
  year={2017}
}

@Article{santambrogio2015optimal,
  author  = {Santambrogio, F.},
  journal = {Birk{\"a}user},
  title   = {Optimal transport for applied mathematicians},
  year    = {2015},
}

@InProceedings{pmlr-v28-nguyen13a,
  title = 	 {Algorithms for Direct 0--1 Loss Optimization in Binary Classification},
  author = 	 {Nguyen, T. and Sanner, S.},
  booktitle = 	 {Proceedings of the 30th International Conference on Machine Learning},
  pages = 	 {1085--1093},
  year = 	 {2013},
  volume = 	 {28},
  number =       {3},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Atlanta, Georgia, USA},
  month = 	 {17--19 Jun},
  publisher = 	 {PMLR},
  }

@inproceedings{zhu2017unpaired,
  title={Unpaired image-to-image translation using cycle-consistent adversarial networks},
  author={Zhu, J-Y and Park, T. and Isola, P. and Efros, A. A.},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={2223--2232},
  year={2017}
}


@InProceedings{pmlr-v70-kim17a,
  title = 	 {Learning to Discover Cross-Domain Relations with Generative Adversarial Networks},
  author = 	 {Kim, T. and Cha, M. and Kim, H. and Lee, J. K. and Kim, J.},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {1857--1865},
  year = 	 {2017},
  editor = 	 {Doina Precup and Yee Whye Teh},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {International Convention Centre, Sydney, Australia},
  month = 	 {06--11 Aug},
  publisher = 	 {PMLR}
  }

@Book{villani2008optimal,
  author    = {Villani, C.},
  publisher = {Springer Berlin Heidelberg},
  title     = {Optimal Transport: Old and New},
  year      = {2008},
}

@Article{Gibbs02onchoosing,
  author  = {Gibbs, A. L. and Su, F. E.},
  journal = {INTERNAT. STATIST. REV.},
  title   = {On choosing and bounding probability metrics},
  year    = {2002},
  pages   = {419--435},
}

@article{zhang19_theory,
  author    = {Zhang, Y. and
               Liu, Y. and
               Long, M. and
               Jordan, M. I.},
  title     = {Bridging Theory and Algorithm for Domain Adaptation},
  journal   = {CoRR},
  volume    = {abs/1904.05801},
  year      = {2019},
  url       = {http://arxiv.org/abs/1904.05801}
}

@InProceedings{long2017JAN,
  author    = {Long, M. and Zhu, H. and Wang, J. and Jordan, M. I.},
  booktitle = {ICML},
  title     = {Deep Transfer Learning with Joint Adaptation Networks},
  year      = {2017},
}

@Article{VAT,
  author  = {{Miyato}, T. and {Maeda}, S. and {Koyama}, M. and {Ishii}, S.},
  journal = {IEEE TPAMI},
  title   = {Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning},
  year    = {2019},
}

@InProceedings{ghifary2016deep,
  author    = {Ghifary, M. and Kleijn, W. B. and Zhang, M. and Balduzzi, D. and Li, W.},
  booktitle = {ECCV},
  title     = {Deep reconstruction-classification networks for unsupervised domain adaptation},
  year      = {2016},
}

@inproceedings{bousmalis2016domain,
  title={Domain separation networks},
  author={Bousmalis, K. and Trigeorgis, G. and Silberman, N. and Krishnan, D. and Erhan, D.},
  booktitle={Advances in neural information processing systems},
  pages={343--351},
  year={2016}
}

@InProceedings{bousmalis2017unsupervised,
  author    = {Bousmalis, K. and Silberman, N. and Dohan, D. and Erhan, D. and Krishnan, D.},
  booktitle = {CVPR},
  title     = {Unsupervised pixel-level domain adaptation with generative adversarial networks},
  year      = {2017},
}

@inproceedings{sener2016learning,
  title={Learning transferrable representations for unsupervised domain adaptation},
  author={Sener, O. and Song, H O and Saxena, A. and Savarese, S.},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2110--2118},
  year={2016}
}

@InProceedings{saito2017asymmetric,
  author    = {{et al.}, K. Saito},
  booktitle = {ICML},
  title     = {Asymmetric tri-training for unsupervised domain adaptation},
  year      = {2017},
}

@Article{vanDerMaaten2008,
  author  = {van der Maaten, L. and Hinton, G.},
  journal = {JMLR},
  title   = {Visualizing Data using {t-SNE}},
  year    = {2008},
}

@InProceedings{chapelle2005semi,
  author    = {Chapelle, O. and Zien, A.},
  booktitle = {AISTATS},
  title     = {Semi-supervised classification by low density separation.},
  year      = {2005},
}

@InCollection{stochastic_ws,
  author    = {Genevay, A. and Cuturi, M. and Peyr\'{e}, G. and Bach, F.},
  booktitle = {NIPS},
  title     = {Stochastic Optimization for Large-scale Optimal Transport},
  year      = {2016},
}

@inproceedings{freund1995desicion,
  title={A desicion-theoretic generalization of on-line learning and an application to boosting},
  author={Freund, Y. and Schapire, R. E.},
  booktitle={European conference on computational learning theory},
  pages={23--37},
  year={1995},
  organization={Springer}
}

@article{friedman2001greedy,
  title={Greedy function approximation: a gradient boosting machine},
  author={Friedman, J. H.},
  journal={Annals of statistics},
  pages={1189--1232},
  year={2001},
  publisher={JSTOR}
}

@article{breiman2001random,
  title={Random forests},
  author={Breiman, L.},
  journal={Machine learning},
  volume={45},
  number={1},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

@inproceedings{xgboost,
author = {Chen, T. and Guestrin, C.},
title = {XGBoost: A Scalable Tree Boosting System},
year = {2016},
booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {785–794},
numpages = {10},
keywords = {large-scale machine learning},
location = {San Francisco, California, USA},
series = {KDD ’16}
}

@article{GroverE17,
  author    = {Aditya G. and Stefano E.},
  title     = {Boosted Generative Models},
  booktitle   = {Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)},
  year      = {2017}
}

@InProceedings{cranko19b,
  title = 	 {Boosted Density Estimation Remastered},
  author = 	 {Cranko, Z. and Nock, R.},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {1416--1425},
  year = 	 {2019},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research}
}

@Article{ganin2016domain,
  author  = {Y. Ganin and E. Ustinova and H. Ajakan and P. Germain and H. Larochelle and F. Laviolette and M. Marchand and V. Lempitsky},
  journal = {JMLR},
  title   = {Domain-Adversarial Training of Neural Networks},
  year    = {2016},
}

@InProceedings{damodaran2018deepjdot,
  author    = {B. B. Damodaran and B. Kellenberger and R. Flamary and D. Tuia and N. Courty},
  booktitle = {ECCV},
  title     = {DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation},
  year      = {2018},
}

@InProceedings{sun2016return,
  author    = {B. Sun and J. Feng and K. Saenko},
  title     = {Return of Frustratingly Easy Domain Adaptation},
  booktitle = {Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
  year      = {2016},
  series    = {AAAI'16},
  pages     = {2058-2065},
  publisher = {AAAI Press},
  location  = {Phoenix, Arizona},
  numpages  = {8},
}

@InProceedings{long2013transfer,
  author    = {M. {Long} and J. {Wang} and G. {Ding} and J. {Sun} and P. S. {Yu}},
  booktitle = {ICCV},
  title     = {Transfer Feature Learning with Joint Distribution Adaptation},
  year      = {2013},
}

@InProceedings{venkateswara2017deephasing,
  author    = {H. Venkateswara and J. Eusebio and S. Chakraborty and S. Panchanathan},
  booktitle = {CVPR},
  title     = {Deep hashing network for unsupervised domain adaptation},
  year      = {2017},
}

@InProceedings{ho2017multilevel,
  author    = {Ho, N. and Nguyen, X. L. and Yurochkin, M. and Bui, H. H. and Huynh, V. and Phung, D.},
  booktitle = {ICML},
  title     = {Multilevel clustering via Wasserstein means},
  year      = {2017},
}

@InProceedings{ho2019probabilistic,
  author    = {Ho, N. and Huynh, V. and Phung, D. and Jordan, M. I.},
  booktitle = {AISTATS},
  title     = {Probabilistic Multilevel Clustering via Composite Transportation Distance},
  year      = {2019},
}

@InProceedings{courty2017joint,
  author    = {Courty, N. and Flamary, R. and Habrard, A. and Rakotomamonjy, A.},
  booktitle = {NIPS},
  title     = {Joint distribution optimal transportation for domain adaptation},
  year      = {2017},
}

@InProceedings{abbeel2004apprenticeship,
  author    = {Abbeel, P. and Ng, A. Y.},
  booktitle = {ICML},
  title     = {Apprenticeship learning via inverse reinforcement learning},
  year      = {2004},
}

@InProceedings{pmlr-v15-ross11a,
  author    = {Ross, S. and Gordon, G. and Bagnell, D.},
  booktitle = {AISTATS},
  title     = {A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning},
  year      = {2011},
}

@InProceedings{ho2016generative,
  author    = {Ho, J. and Ermon, S.},
  booktitle = {NIPS},
  title     = {Generative adversarial imitation learning},
  year      = {2016},
}

@Article{peyre2019computational,
  author  = {Peyr{\'e}, G. and Cuturi, M. and others},
  journal = {Foundations and Trends{\textregistered} in Machine Learning},
  title   = {Computational optimal transport},
  year    = {2019},
}

@InProceedings{batch_norm,
  author    = {Ioffe, S. and Szegedy, C.},
  booktitle = {Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37},
  title     = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
  year      = {2015},
  pages     = {448--456},
  location  = {Lille, France},
  numpages  = {9},
}

@InProceedings{RedkoCFT19,
  author    = {Redko, I. and Courty, N. and Flamary, R. and Tuia, D.},
  booktitle = {AISTATS},
  title     = {Optimal Transport for Multi-source Domain Adaptation under Target Shift},
  year      = {2019},
}

@InProceedings{gong2012geodesic,
  author    = {B. {Gong} and Y. {Shi} and F. {Sha} and K. {Grauman}},
  booktitle = {CVPR},
  title     = {Geodesic flow kernel for unsupervised domain adaptation},
  year      = {2012},
}

@Article{pan2011tca,
  author  = {S. J. {Pan} and I. W. {Tsang} and J. T. {Kwok} and Q. {Yang}},
  journal = {IEEE Transactions on Neural Networks},
  title   = {Domain Adaptation via Transfer Component Analysis},
  year    = {2011},
}

@Article{Lecun1998mnist,
  author  = {Y. {Lecun} and L. {Bottou} and Y. {Bengio} and P. {Haffner}},
  journal = {Proceedings of the IEEE},
  title   = {Gradient-based learning applied to document recognition},
  year    = {1998},
}

@InProceedings{netzer2011svhn,
  author    = {Y. Netzer and T. Wang and A. Coates and A. Bissacco and B. Wu and A. Y. Ng},
  booktitle = {NIPS Workshop on Deep Learning and Unsupervised Feature Learning},
  title     = {Reading Digits in Natural Images with Unsupervised Feature Learning},
  year      = {2011},
}

@InProceedings{moiseev2013synsign,
  author    = {Moiseev, B. and Konev, A. and Chigorin, A. and Konushin, A.},
  title     = {Evaluation of Traffic Sign Recognition Methods Trained on Synthetically Generated Data},
  booktitle = {15th International Conference on Advanced Concepts for Intelligent Vision Systems - Volume 8192},
  year      = {2013},
  series    = {ACIVS 2013},
  pages     = {576--583},
  address   = {Berlin, Heidelberg},
  publisher = {Springer-Verlag},
  isbn      = {9783319028941},
  keywords  = {synthetic data, traffic sign recognition, nearest neighbor search},
  location  = {Poznaundefined, Poland},
  numpages  = {8},
}

@InProceedings{stallkamp2011gtsrb,
  author    = {J. {Stallkamp} and M. {Schlipsing} and J. {Salmen} and C. {Igel}},
  title     = {The German Traffic Sign Recognition Benchmark: A multi-class classification competition},
  booktitle = {The 2011 International Joint Conference on Neural Networks},
  year      = {2011},
  pages     = {1453-1460},
}

@Article{srivastava2014dropout,
  author     = {Srivastava, N. and Hinton, G. and Krizhevsky, A. and Sutskever, I. and Salakhutdinov, R.},
  journal    = {J. Mach. Learn. Res.},
  title      = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
  year       = {2014},
  issn       = {1532-4435},
  month      = jan,
  number     = {1},
  pages      = {1929-1958},
  volume     = {15},
  issue_date = {January 2014},
  keywords   = {neural networks, model combination, deep learning, regularization},
  numpages   = {30},
  publisher  = {JMLR.org},
}

@Misc{chollet2015keras,
  author       = {Chollet, F. and others},
  title        = {Keras},
  howpublished = {\url{https://keras.io}},
  year         = {2015},
}

@InProceedings{yujia2019onscalable,
  author    = {Y. Xie and M. Chen and H. Jiang and T. Zhao and H. Zha},
  booktitle = {ICML},
  title     = {On Scalable and Efficient Computation of Large Scale Optimal Transport},
  year      = {2019},
}

@InProceedings{chenyu2019swd,
  author    = {C. Lee and T. Batra and M. H. Baig and D. Ulbricht},
  booktitle = {CVPR},
  title     = {Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation},
  year      = {2019},
}

@InProceedings{tzeng2017ADDA,
  author    = {E. {Tzeng} and J. {Hoffman} and K. {Saenko} and T. {Darrell}},
  booktitle = {CVPR},
  title     = {Adversarial Discriminative Domain Adaptation},
  year      = {2017},
}

@InProceedings{hoffman2018cycada,
  author    = {Hoffman, J. and Tzeng, E. and Park, T. and Zhu, J-Y and Isola, P. and Saenko, K. and Efros, A. and Darrell, T.},
  booktitle = {ICML},
  title     = {CyCADA: Cycle-Consistent Adversarial Domain Adaptation},
  year      = {2018},
}

@InProceedings{xie2018mstn,
  author    = {S. Xie and Z. Zheng and L. Chen and C. Chen},
  booktitle = {ICML},
  title     = {Learning Semantic Representations for Unsupervised Domain Adaptation},
  year      = {2018},
}

@InCollection{long2018cdan,
  author    = {Long, M. and Cao, Z. and Wang, J. and Jordan, M. I.},
  booktitle = {NeurIPS},
  title     = {Conditional Adversarial Domain Adaptation},
  year      = {2018},
}

@InProceedings{saito2018mcd,
  author    = {Saito, K. and Watanabe, K. and Ushiku, Y. and Harada, T.},
  booktitle = {CVPR},
  title     = {Maximum Classifier Discrepancy for Unsupervised Domain Adaptation},
  year      = {2018},
}

@InProceedings{chen2019pfa,
  author    = {C. Chen and W. Xie and W. Huang and Y. Rong and X. Ding and Y. Huang and T. Xu and J. Huang},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}},
  title     = {Progressive Feature Alignment for Unsupervised Domain Adaptation},
  year      = {2019},
  pages     = {627--636},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl    = {https://dblp.org/rec/conf/cvpr/ChenXHRD0XH19.bib},
  doi       = {10.1109/CVPR.2019.00072},
  timestamp = {Mon, 20 Jan 2020 15:36:04 +0100},
  url       = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Chen\_Progressive\_Feature\_Alignment\_for\_Unsupervised\_Domain\_Adaptation\_CVPR\_2019\_paper.html},
}

@InProceedings{kim2019gpda,
  author    = {M. {Kim} and P. {Sahu} and B. {Gholami} and V. {Pavlovic}},
  booktitle = {CVPR},
  title     = {Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach},
  year      = {2019},
}

@InProceedings{chen2018learning,
  author    = {Chen, J. and Song, L. and Wainwright, M. and Jordan, M.},
  booktitle = {ICML},
  title     = {Learning to Explain: An Information-Theoretic Perspective on Model Interpretation},
  year      = {2018},
}

@InProceedings{deng2019cluster,
  author    = {Deng, Z. and Luo, Y. and Zhu, J.},
  booktitle = {ICCV},
  title     = {Cluster Alignment With a Teacher for Unsupervised Domain Adaptation},
  year      = {2019},
}

@InProceedings{goodfellow2014explaining,
  author    = {I. Goodfellow and J. Shlens and C. Szegedy},
  booktitle = {International Conference on Learning Representations},
  title     = {Explaining and Harnessing Adversarial Examples},
  year      = {2015},
  url       = {http://arxiv.org/abs/1412.6572},
}

@InProceedings{liang2020shot,
  author    = {{et al.}, J. Liang},
  booktitle = {ICML},
  title     = {Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation},
  year      = {2020},
}

@InProceedings{simonyan2014deep,
  author    = {K. Simonyan and A. Zisserman},
  booktitle = {3rd International Conference on Learning Representations, {ICLR}},
  title     = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
  year      = {2015},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl    = {https://dblp.org/rec/journals/corr/SimonyanZ14a.bib},
  timestamp = {Wed, 17 Jul 2019 10:40:54 +0200},
  url       = {http://arxiv.org/abs/1409.1556},
}

@InProceedings{tang2019discriminative,
  author    = {H. Tang and K. Jia},
  booktitle = {Association for the Advancement of Artificial Intelligence (AAAI)},
  title     = {Discriminative Adversarial Domain Adaptation},
  year      = {2020},
}

@InProceedings{TzengHDS15,
  author    = {E. {Tzeng} and J. {Hoffman} and T. {Darrell} and K. {Saenko}},
  booktitle = {ICCV},
  title     = {Simultaneous Deep Transfer Across Domains and Tasks},
  year      = {2015},
}

@InCollection{long2016rtn,
  author    = {Long, M. and Zhu, H. and Wang, J. and Jordan, M. I.},
  booktitle = {NIPS},
  title     = {Unsupervised Domain Adaptation with Residual Transfer Networks},
  year      = {2016},
}

@InProceedings{pei2018mada,
  author    = {Zhongyi Pei and Zhangjie Cao and Mingsheng Long and Jianmin Wang},
  booktitle = {AAAI},
  title     = {Multi-Adversarial Domain Adaptation},
  year      = {2018},
}

@Article{sankaranarayanan2018gta,
  author  = {S. Sankaranarayanan and Y. Balaji and Carlos D. Castillo and R. Chellappa},
  journal = {CVPR},
  title   = {Generate to Adapt: Aligning Domains Using Generative Adversarial Networks},
  year    = {2018},
}

@InProceedings{zhang2018ican,
  author    = {W. {Zhang} and W. {Ouyang} and W. {Li} and D. {Xu}},
  booktitle = {CVPR},
  title     = {Collaborative and Adversarial Network for Unsupervised Domain Adaptation},
  year      = {2018},
}

@InProceedings{chen2019JointDA,
  author    = {C. Chen and Z. Chen and B. Jiang and X. Jin},
  booktitle = {AAAI},
  title     = {Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation},
  year      = {2019},
}

@InProceedings{wang2019tada,
  author    = {X. Wang and L. Li and W. Ye and M. Long and J. Wang},
  booktitle = {AAAI},
  title     = {Transferable Attention for Domain Adaptation},
  year      = {2019},
}

@Article{wang2018meda,
  author  = {J. Wang and Wenjie Feng and Y. Chen and H. Yu and M. Huang and Philip S. Yu},
  journal = {ACM international conference on Multimedia},
  title   = {Visual Domain Adaptation with Manifold Embedded Distribution Alignment},
  year    = {2018},
}

@InProceedings{wang2019capls,
  author    = {Q. {Wang} and P. {Bu} and T. P. {Breckon}},
  booktitle = {2019 International Joint Conference on Neural Networks (IJCNN)},
  title     = {Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition},
  year      = {2019},
  pages     = {1-8},
}

@InProceedings{pan2019tpn,
  author    = {Y. {Pan} and T. {Yao} and Y. {Li} and Y. {Wang} and C. {Ngo} and T. {Mei}},
  booktitle = {CVPR},
  title     = {Transferrable Prototypical Networks for Unsupervised Domain Adaptation},
  year      = {2019},
}

@InProceedings{wang2020spl,
  author    = {Q. Wang and T. P. Breckon},
  booktitle = {AAAI},
  title     = {Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling},
  year      = {2020},
}

@InProceedings{xu2020reliable,
  author    = {Xu, R. and Liu, P. and Wang, L. and Chen, C. and Wang, J.},
  booktitle = {CVPR},
  title     = {Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation},
  year      = {2020},
}

@InProceedings{he2016resnet,
  author    = {K. {He} and X. {Zhang} and S. {Ren} and J. {Sun}},
  booktitle = {CVPR},
  title     = {Deep Residual Learning for Image Recognition},
  year      = {2016},
}

@InProceedings{saenko20office31,
  author    = {Saenko, K. and Kulis, B. and Fritz, M. and Darrell, T.},
  booktitle = {ECCV},
  title     = {Adapting Visual Category Models to New Domains},
  year      = {2010},
}

@InProceedings{wang2020learning,
  author    = {H. Wang and M. Xu and B. Ni and W. Zhang},
  booktitle = {ECCV},
  title     = {Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation},
  year      = {2020},
}

@InProceedings{sun2016coral,
  author    = {Sun, B. and Saenko, K.},
  booktitle = {ECCV},
  title     = {Deep CORAL: Correlation Alignment for Deep Domain Adaptation},
  year      = {2016},
}

@InCollection{zhan2018MDAN,
  author    = {Zhao, H. and Zhang, S. and Wu, G. and Moura, J. M. F. and Costeira, J. P. and Gordon, G. J},
  booktitle = {NeurIPS},
  title     = {Adversarial Multiple Source Domain Adaptation},
  year      = {2018},
}

@InProceedings{xu2018DCTN,
  author    = {R. {Xu} and Z. {Chen} and W. {Zuo} and J. {Yan} and L. {Lin}},
  booktitle = {CVPR},
  title     = {Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift},
  year      = {2018},
}

@InProceedings{peng2019M3SDA,
  author    = {Peng, X. and Bai, Q. and Xia, X. and Huang, Z. and Saenko, K. and Wang, B.},
  booktitle = {ICCV},
  title     = {Moment matching for multi-source domain adaptation},
  year      = {2019},
}

@InProceedings{zhao2020MDDA,
  author    = {S. Zhao and G. Wang and S. Zhang and Y. Gu and Y. Li and Z. Song and P. Xu and R. Hu and H. Chai and K. Keutzer},
  booktitle = {AAAI},
  title     = {Multi-Source Distilling Domain Adaptation},
  year      = {2020},
}

@Article{hull1994usps,
  author  = {J. J. {Hull}},
  journal = {IEEE TPAMI},
  title   = {A database for handwritten text recognition research},
  year    = {1994},
}

@Article{griffin2007Caltech256,
  author  = {Griffin, G. and Holub, A. and Perona, P.},
  journal = {CalTech Report},
  title   = {Caltech-256 Object Category Dataset},
  year    = {2007},
  month   = {03},
}

@InProceedings{xu2015sat,
  author = {K. Xu and J. Ba and R. Kiros and K. Cho and A. Courville and R. Salakhudinov and R. Zemel and Y. Bengio},
  title  = {Show, Attend and Tell: Neural Image Caption Generation with Visual Attention},
  year   = {2015},
  series = {Proceedings of Machine Learning Research},
}

@InCollection{ren2015fasterRCNN,
  author    = {Ren, S. and He, K. and Girshick, R. and Sun, J.},
  booktitle = {NIPS},
  title     = {Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
  year      = {2015},
}

@Article{shelhamer2017FCNSS,
  author  = {Shelhamer, E. and Long, J. and Darrell, T.},
  journal = {IEEE TPAMI},
  title   = {Fully Convolutional Networks for Semantic Segmentation},
  year    = {2017},
}

@InProceedings{cao2017realtime,
  author    = {Cao, Z. and Simon, T. and Wei, S. and Sheikh, Y.},
  booktitle = {CVPR},
  title     = {Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields},
  year      = {2017},
}

@Book{candela2009datashift,
  author    = {Quionero-Candela, J. and Sugiyama, M. and Schwaighofer, A. and Lawrence, N. D.},
  publisher = {The MIT Press},
  title     = {Dataset Shift in Machine Learning},
  year      = {2009},
}

@Article{tzeng2015ddc,
  author  = {E. Tzeng and J. Hoffman and N. Zhang and K. Saenko and T. Darrell},
  journal = {CoRR},
  title   = {Deep Domain Confusion: Maximizing for Domain Invariance},
  year    = {2014},
}

@Article{yan2017MindTC,
  author  = {H. Yan and Yukang Ding and P. Li and Qilong Wang and Yong Xu and W. Zuo},
  journal = {CVPR},
  title   = {Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation},
  year    = {2017},
}

@InProceedings{xu2020adversarial,
  author    = {M. Xu and J. Zhang and B. Ni and T. Li and C. Wang and Q. Tian and W. Zhang},
  booktitle = {AAAI},
  title     = {Adversarial Domain Adaptation with Domain Mixup},
  year      = {2020},
}

@InProceedings{xu2020crossdomain,
  author    = {M. Xu and H. Wang and B. Ni and Q. Tian and W. Zhang},
  booktitle = {CVPR},
  title     = {Cross-Domain Detection via Graph-Induced Prototype Alignment},
  year      = {2020},
}

@InCollection{crammer2007msda,
  author    = {Crammer, K. and Kearns, M. and J. Wortman},
  booktitle = {NIPS},
  title     = {Learning from Multiple Sources},
  year      = {2007},
}

@InCollection{hoffman2018theorymsda,
  author    = {Hoffman, J. and Mohri, M. and Zhang, N.},
  booktitle = {NeurIPS},
  publisher = {Curran Associates, Inc.},
  title     = {Algorithms and Theory for Multiple-Source Adaptation},
  year      = {2018},
}

@InProceedings{duan2012exploit,
  author    = {L. {Duan} and D. {Xu} and S. {Chang}},
  booktitle = {CVPR},
  title     = {Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach},
  year      = {2012},
}

@InProceedings{courty2014darot,
  author    = {Courty, N. and Flamary, R. and Tuia, D.},
  booktitle = {Machine Learning and Knowledge Discovery in Databases},
  title     = {Domain Adaptation with Regularized Optimal Transport},
  year      = {2014},
}

@InProceedings{yan2018semiot,
  author    = {Y. Yan and W. Li and H. Wu and H. Min and M. Tan and Q. Wu},
  booktitle = {IJCAI},
  title     = {Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation},
  year      = {2018},
}

@InCollection{grandvalet2015entropy,
  author    = {Grandvalet, Y. and Bengio, Y.},
  booktitle = {NIPS},
  title     = {Semi-supervised Learning by Entropy Minimization},
  year      = {2005},
}

@InCollection{kumar2018co,
  author    = {Kumar, A. and Sattigeri, P. and Wadhawan, K. and Karlinsky, L. and Feris, R. and Freeman, B. and Wornell, G.},
  booktitle = {NeurIPS},
  title     = {Co-regularized Alignment for Unsupervised Domain Adaptation},
  year      = {2018},
}

@Article{sugiyama2007cov,
  author  = {Sugiyama, M. and Krauledat, M. and M\"{u}ller, K.},
  journal = {JMLR},
  title   = {Covariate Shift Adaptation by Importance Weighted Cross Validation},
  year    = {2007},
}

@Misc{turrisi2020multisource,
  author        = {R. Turrisi and R. Flamary and A. Rakotomamonjy and M. Pontil},
  title         = {Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport},
  year          = {2020},
  archiveprefix = {arXiv},
  eprint        = {2006.12938},
  primaryclass  = {cs.LG},
}

@InProceedings{donahue2014decaf,
  author    = {J. Donahue and Y. Jia and O. Vinyals and J. Hoffman and N. Zhang and E. Tzeng and T. Darrell},
  title     = {DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition},
  year      = {2014},
  address   = {Bejing, China},
  editor    = {Eric P. Xing and Tony Jebara},
  month     = {22--24 Jun},
  number    = {1},
  pages     = {647--655},
  publisher = {PMLR},
  series    = {Proceedings of Machine Learning Research},
  volume    = {32},
  abstract  = {We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks. Our generic tasks may differ significantly from the originally trained tasks and there may be insufficient labeled or unlabeled data to conventionally train or adapt a deep architecture to the new tasks. We investigate and visualize the semantic clustering of deep convolutional features with respect to a variety of such tasks, including scene recognition, domain adaptation, and fine-grained recognition challenges. We compare the efficacy of relying on various network levels to define a fixed feature, and report novel results that significantly outperform the state-of-the-art on several important vision challenges. We are releasing DeCAF, an open-source implementation of these deep convolutional activation features, along with all associated network parameters to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.},
  pdf       = {http://proceedings.mlr.press/v32/donahue14.pdf},
  url       = {http://proceedings.mlr.press/v32/donahue14.html},
}

@Misc{rakotomamonjy2020match,
  author        = {A. Rakotomamonjy and R. Flamary and G. Gasso and M. Z. Alaya and M. Berar and N. Courty},
  title         = {Match and Reweight Strategy for Generalized Target Shift},
  year          = {2020},
  archiveprefix = {arXiv},
  eprint        = {2006.08161},
  primaryclass  = {cs.LG},
}

@InProceedings{kingma2014adam,
  author    = {D. P. Kingma and J. Ba},
  booktitle = {ICLR},
  title     = {Adam: {A} Method for Stochastic Optimization},
  year      = {2015},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl    = {https://dblp.org/rec/journals/corr/KingmaB14.bib},
  timestamp = {Thu, 25 Jul 2019 14:25:37 +0200},
}

@InProceedings{pinheiro2018simnet,
  author    = {Pinheiro, Pedro O.},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Unsupervised Domain Adaptation With Similarity Learning},
  year      = {2018},
  month     = {June},
}

@Article{chen2020ALDA,
  author       = {Chen, Minghao and Zhao, Shuai and Liu, Haifeng and Cai, Deng},
  journal      = {Proceedings of the AAAI Conference on Artificial Intelligence},
  title        = {Adversarial-Learned Loss for Domain Adaptation},
  year         = {2020},
  month        = {Apr.},
  number       = {04},
  pages        = {3521-3528},
  volume       = {34},
  abstractnote = {&lt;p&gt;Recently, remarkable progress has been made in learning transferable representation across domains. Previous works in domain adaptation are majorly based on two techniques: domain-adversarial learning and self-training. However, domain-adversarial learning only aligns feature distributions between domains but does not consider whether the target features are discriminative. On the other hand, self-training utilizes the model predictions to enhance the discrimination of target features, but it is unable to explicitly align domain distributions. In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA). We first analyze the pseudo-label method, a typical self-training method. Nevertheless, there is a gap between pseudo-labels and the ground truth, which can cause incorrect training. Thus we introduce the confusion matrix, which is learned through an adversarial manner in ALDA, to reduce the gap and align the feature distributions. Finally, a new loss function is auto-constructed from the learned confusion matrix, which serves as the loss for unlabeled target samples. Our ALDA outperforms state-of-the-art approaches in four standard domain adaptation datasets. Our code is available at https://github.com/ZJULearning/ALDA.&lt;/p&gt;},
  doi          = {10.1609/aaai.v34i04.5757},
  url          = {https://ojs.aaai.org/index.php/AAAI/article/view/5757},
}

@InProceedings{Koch2015SiameseNN,
  author = {Koch, G. and Zemel, R. and Salakhutdinov, R.},
  title  = {Siamese Neural Networks for One-shot Image Recognition},
  year   = {2015},
}

@Article{polyak1992acce,
  author    = {Polyak, B. T. and Juditsky, A. B.},
  journal   = {SIAM J. Control Optim.},
  title     = {Acceleration of Stochastic Approximation by Averaging},
  year      = {1992},
  address   = {USA},
  doi       = {10.1137/0330046},
  publisher = {Society for Industrial and Applied Mathematics},
}

@Article{pan2010surveyDA,
  author  = {S. J. {Pan} and Q. {Yang}},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  title   = {A Survey on Transfer Learning},
  year    = {2010},
  number  = {10},
  pages   = {1345-1359},
  volume  = {22},
  doi     = {10.1109/TKDE.2009.191},
}

@Misc{zellinger2019central,
  author        = {W. Zellinger and T. Grubinger and E. Lughofer and T. Natschläger and S. Saminger-Platz},
  title         = {Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning},
  year          = {2019},
  archiveprefix = {arXiv},
  eprint        = {1702.08811},
  primaryclass  = {stat.ML},
}

@InCollection{rahman2020onminimum,
  author    = {M. M. Rahman and C. Fookes and M. Baktashmotlagh and S. Sridharan},
  booktitle = {Domain Adaptation for Visual Understanding},
  publisher = {Springer},
  title     = {On Minimum Discrepancy Estimation for Deep Domain Adaptation},
  year      = {2020},
  address   = {Switzerland},
  editor    = {Richa Singh and Mayank Vatsa and Vishal M. Patel and Nalini Ratha},
  month     = {January},
  pages     = {81--94},
  abstract  = {In the presence of large sets of labeled data, Deep Learning DL has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification and recognition tasks. However, DL cannot always perform well when the training and testing images come from different distributions or in the presence of domain shift between training and testing images. They also suffer in the absence of labeled input data. Domain adaptation DA methods have been proposed to make up the poor performance due to domain shift. In this chapter, we present a new unsupervised deep domain adaptation method based on the alignment of second-order statistics covariances as well as maximum mean discrepancy of the source and target data with a two-stream Convolutional Neural Network CNN. We demonstrate the ability of the proposed approach to achieve state-of-the-art performance for image classification on three benchmark domain adaptation datasets: Office-31 [27], Office-Home [37] and Office-Caltech [8].},
  doi       = {10.1007/978-3-030-30671-7\_6},
  url       = {https://eprints.qut.edu.au/200991/},
}

@InProceedings{morerio2018minimalentropy,
  author    = {P. Morerio and J. Cavazza and V. Murino},
  booktitle = {International Conference on Learning Representations},
  title     = {Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation},
  year      = {2018},
  url       = {https://openreview.net/forum?id=rJWechg0Z},
}

@InProceedings{zhuang2015kl,
  author    = {Zhuang, F. and Cheng, X. and Luo, P. and Pan, S. Jialin and H. , Qing},
  booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence},
  title     = {Supervised Representation Learning: Transfer Learning with Deep Autoencoders},
  year      = {2015},
  pages     = {4119--4125},
  publisher = {AAAI Press},
  series    = {IJCAI'15},
  abstract  = {Transfer learning has attracted a lot of attention in the past decade. One crucial research issue in transfer learning is how to find a good representation for instances of different domains such that the divergence between domains can be reduced with the new representation. Recently, deep learning has been proposed to learn more robust or higherlevel features for transfer learning. However, to the best of our knowledge, most of the previous approaches neither minimize the difference between domains explicitly nor encode label information in learning the representation. In this paper, we propose a supervised representation learning method based on deep autoencoders for transfer learning. The proposed deep autoencoder consists of two encoding layers: an embedding layer and a label encoding layer. In the embedding layer, the distance in distributions of the embedded instances between the source and target domains is minimized in terms of KL-Divergence. In the label encoding layer, label information of the source domain is encoded using a softmax regression model. Extensive experiments conducted on three real-world image datasets demonstrate the effectiveness of our proposed method compared with several state-of-the-art baseline methods.},
  isbn      = {9781577357384},
  location  = {Buenos Aires, Argentina},
  numpages  = {7},
}

@InProceedings{kang2019can,
  author    = {G. {Kang} and L. {Jiang} and Y. {Yang} and A. G. {Hauptmann}},
  booktitle = {CVPR},
  title     = {Contrastive Adaptation Network for Unsupervised Domain Adaptation},
  year      = {2019},
}

@Misc{zhang2015deep,
  author        = {X. Zhang and F. X. Yu and S. Chang and S. Wang},
  title         = {Deep Transfer Network: Unsupervised Domain Adaptation},
  year          = {2015},
  archiveprefix = {arXiv},
  eprint        = {1503.00591},
  primaryclass  = {cs.CV},
}

@InProceedings{chen2018reweight,
  author    = {Q. {Chen} and Y. {Liu} and Z. {Wang} and I. {Wassell} and K. {Chetty}},
  booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  title     = {Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation},
  year      = {2018},
  pages     = {7976-7985},
  doi       = {10.1109/CVPR.2018.00832},
}

@InProceedings{hu2018duplex,
  author    = {Hu, L. and Kan, M. and Shan, S. and Chen, X.},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Duplex Generative Adversarial Network for Unsupervised Domain Adaptation},
  year      = {2018},
  month     = {June},
}

@InProceedings{volpi2018afa,
  author    = {Volpi, R. and Morerio, P. and Savarese, S. and Murino, V.},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Adversarial Feature Augmentation for Unsupervised Domain Adaptation},
  year      = {2018},
  month     = {June},
}

@InProceedings{chang2019domainBN,
  author    = {Chang, W. and You, T. and Seo, S. and Kwak, S. and Han, B.},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Domain-Specific Batch Normalization for Unsupervised Domain Adaptation},
  year      = {2019},
  month     = {June},
}

@InProceedings{chen2019progressive,
  author    = {Chen, Chaoqi and Xie, Weiping and Huang, Wenbing and Rong, Yu and Ding, Xinghao and Huang, Yue and Xu, Tingyang and Huang, Junzhou},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Progressive Feature Alignment for Unsupervised Domain Adaptation},
  year      = {2019},
  month     = {June},
}

@InProceedings{kurmi2019attending,
  author    = {Kurmi, V. K. and Kumar, S. and Namboodiri, V. P.},
  booktitle = {CVPR},
  title     = {Attending to Discriminative Certainty for Domain Adaptation},
  year      = {2019},
  month     = {June},
}

@InProceedings{ma2019gcan,
  author    = {Ma, X. and Zhang, T. and Xu, C.},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation},
  year      = {2019},
  month     = {June},
}

@InProceedings{roy2019fw,
  author    = {Roy, S. and Siarohin, A. and Sangineto, E. and Bulo, S. R. and Sebe, N. and Ricci, E.},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss},
  year      = {2019},
  month     = {June},
}

@InProceedings{xu2019dsne,
  author    = {Xu, Xiang and Zhou, Xiong and Venkatesan, Ragav and Swaminathan, Gurumurthy and Majumder, Orchid},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding},
  year      = {2019},
  month     = {June},
}

@InProceedings{cui2020bridge,
  author    = {Cui, S. and Wang, S. and Zhuo, J. and Su, C. and Huang, Q. and Tian, Q.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Gradually Vanishing Bridge for Adversarial Domain Adaptation},
  year      = {2020},
  month     = {June},
}

@InProceedings{dong2020els,
  author    = {Dong, Jiahua and Cong, Yang and Sun, Gan and Zhong, Bineng and Xu, Xiaowei},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation},
  year      = {2020},
  month     = {June},
}

@InProceedings{hu2020HGS,
  author    = {Hu, L. and Kan, M. and Shan, S. and Chen, X.},
  booktitle = {CVPR},
  title     = {Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization},
  year      = {2020},
}

@InProceedings{li2020withoutSource,
  author    = {Li, R. and Jiao, Q. and Cao, W. and Wong, H. and Wu, S.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Model Adaptation: Unsupervised Domain Adaptation Without Source Data},
  year      = {2020},
  month     = {June},
}

@InProceedings{lu2020stochastic,
  author    = {Lu, Z. and Yang, Y. and Zhu, X. and Liu, C. and Song, Y. and Xiang, T.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Stochastic Classifiers for Unsupervised Domain Adaptation},
  year      = {2020},
  month     = {June},
}

@InProceedings{tang2020SRDC,
  author    = {Tang, H. and Chen, K. and Jia, K.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering},
  year      = {2020},
  month     = {June},
}

@InProceedings{ye2020lightweight,
  author    = {Ye, S. and Wu, K. and Zhou, M. and Yang, Y. and Tan, S. H. and Xu, K. and Song, J. and Bao, C. and Ma, K.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  title     = {Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation},
  year      = {2020},
  month     = {June},
}

@InProceedings{li2020enhanceOT,
  author    = {Li, M. and Zhai, Y. and Luo, Y. and Ge, P. and Ren, C.},
  booktitle = {CVPR},
  title     = {Enhanced Transport Distance for Unsupervised Domain Adaptation},
  year      = {2020},
}

@InProceedings{duan2018daml,
  author    = {Y. {Duan} and W. {Zheng} and X. {Lin} and J. {Lu} and J. {Zhou}},
  booktitle = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  title     = {Deep Adversarial Metric Learning},
  year      = {2018},
  pages     = {2780-2789},
  doi       = {10.1109/CVPR.2018.00294},
}

@InProceedings{zhang2019symnets,
  author    = {Zhang, Y. and Tang, H. and Jia, K. and Tan, M.},
  booktitle = {CVPR},
  title     = {Domain-Symmetric Networks for Adversarial Domain Adaptation},
  year      = {2019},
}

@InProceedings{shen2018ws,
  author    = {J. Shen and Y. Qu and W. Zhang and Y. Yu},
  booktitle = {AAAI},
  title     = {Wasserstein Distance Guided Representation Learning for Domain Adaptation},
  year      = {2018},
  publisher = {{AAAI} Press},
}

@InProceedings{van2019dan,
  author    = {V. Nguyen and T. Le and T. Le and K. Nguyen and O. De Vel and P. Montague and L. Qu and D. Phung},
  booktitle = {IJCNN},
  title     = {Deep Domain Adaptation for Vulnerable Code Function Identification},
  year      = {2019},
}

@InProceedings{van2020dualdan,
  author    = {Nguyen, V. and Le, T. and De Vel, O. and Montague, P. and Grundy, J. and Phung, D.},
  booktitle = {PAKDD},
  title     = {Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection},
  year      = {2020},
}

@InProceedings{tuan2021tidot,
  author    = {Nguyen, T. and Le, T. and Zhao, H. and Tran, H. Q. and Nguyen, T. and Phung, D.},
  booktitle = {IJCAI},
  title     = {TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport},
  year      = {2021},
}

@InProceedings{le2021lamda,
  author    = {Le, T. and Nguyen, T. and Ho, N. and Bui, H. and Phung, D.},
  booktitle = {ICML},
  title     = {LAMDA: Label Matching Deep Domain Adaptation},
  year      = {2021},
}

@InProceedings{caputo2014ImageCLEF,
  author    = {Caputo, B. and M{\"u}ller, H. and Martinez-Gomez, J. and Villegas, M. and Acar, B. and Patricia, N. and Marvasti, N. and {\"U}sk{\"u}darl{\i}, S. and Paredes, R. and Cazorla, M. and Garcia-Varea, I. and Morell, V.},
  title     = {ImageCLEF 2014: Overview and Analysis of the Results},
  year      = {2014},
  publisher = {Springer International Publishing},
}

@InProceedings{pmlr-v97-chen19i,
  author = {Chen, X. and Wang, S. and Long, M. and Wang, J.},
  title  = {Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation},
  year   = {2019},
  editor = {Kamalika Chaudhuri and Ruslan Salakhutdinov},
  pages  = {1081--1090},
  series = {Proceedings of Machine Learning Research},
  volume = {97},
}

@InProceedings{Wang2019CDAN,
  author    = {Wang, X. and Jin, Y. and Long, M. and Wang, J. and Jordan, M. I.},
  booktitle = {Advances in Neural Information Processing Systems},
  title     = {Transferable Normalization: Towards Improving Transferability of Deep Neural Networks},
  year      = {2019},
}

@Article{wang2019classaware,
  author        = {Z. Wang and B. Jing and Y. Ni and N. Dong and P. Xie and E. P. Xing},
  journal       = {CoRR},
  title         = {Adversarial Domain Adaptation Being Aware of Class Relationships},
  year          = {2019},
  volume        = {abs/1905.11931},
  archiveprefix = {arXiv},
}

@InProceedings{fend2021KD3A,
  author    = {Feng, H. and You, Z. and Chen, M. and Zhang, T. and Zhu, M. and Wu, F. and Wu, C. and Chen, W.},
  booktitle = {ICML},
  title     = {KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation},
  year      = {2021},
  pages     = {3274--3283},
}

@Misc{hinton2015distilling,
  author        = {G. Hinton and O. Vinyals and J. Dean},
  title         = {Distilling the Knowledge in a Neural Network},
  year          = {2015},
  archiveprefix = {arXiv},
  eprint        = {1503.02531},
  primaryclass  = {stat.ML},
}

@InProceedings{cuturi2013Sinkhorn,
  author    = {Cuturi, M.},
  booktitle = {Advances in Neural Information Processing Systems},
  title     = {Sinkhorn Distances: Lightspeed Computation of Optimal Transport},
  year      = {2013},
  publisher = {Curran Associates, Inc.},
  volume    = {26},
}

@InProceedings{pan2008transferlearning,
  author    = {Pan, S. J. and Kwok, J. T. and Yang, Q.},
  booktitle = {AAAI},
  title     = {Transfer Learning via Dimensionality Reduction},
  year      = {2008},
  isbn      = {9781577353683},
  location  = {Chicago, Illinois},
  numpages  = {6},
}

@InProceedings{tian2020Contrastive,
  author    = {Y. Tian and D. Krishnan and P. Isola},
  booktitle = {ICLR},
  title     = {Contrastive Representation Distillation},
  year      = {2020},
}

@InProceedings{tejankar2021isd,
  author    = {Tejankar, A. and Koohpayegani, S. A. and Pillai, V. and Favaro, P. and Pirsiavash, H.},
  booktitle = {ICCV},
  title     = {ISD: Self-Supervised Learning by Iterative Similarity Distillation},
  year      = {2021},
}

@Misc{yang2021casting,
  author        = {S. Yang and Y. Wang and J. van de Weijer and L. Herranz and S. Jui},
  title         = {Casting a BAIT for Offline and Online Source-free Domain Adaptation},
  year          = {2021},
  archiveprefix = {arXiv},
  eprint        = {2010.12427},
  primaryclass  = {cs.CV},
}

@InProceedings{tachet2020domain,
  author    = {Tachet des Combes, R. and Zhao, H. and Wang, Y.-X. and Gordon, G.},
  booktitle = {Advances in Neural Information Processing Systems},
  title     = {Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift},
  year      = {2020},
}

@InProceedings{tuan2021most,
  author    = {Nguyen, T. and Le, T. and Zhao, H. and Tran, Q. H. and Nguyen, T. and Phung, D.},
  booktitle = {UAI},
  title     = {Most: multi-source domain adaptation via optimal transport for student-teacher learning},
  year      = {2021},
  month     = {27--30 Jul},
  publisher = {PMLR},
}

@InProceedings{va2021stem,
  author    = {Nguyen, V. and Nguyen, T. and Le, T. and Tran, Q. H. and Phung, D.},
  booktitle = {ICCV},
  title     = {STEM: An approach to Multi-source Domain Adaptation with Guarantees},
  year      = {2021},
}

@InProceedings{thanh2022particle,
  author    = {Nguyen-Duc, T. and Le, T. and Zhao, H. and Cai, J. and Phung, D.},
  title     = {Particle-based Adversarial Local Distribution Regularization},
  year      = {2022},
  publisher = {PMLR},
}

@Misc{hoang2022global,
  author    = {Phan, H. and Le, T. and Phung, T. and Bui, T. and Ho, N. and Phung, D.},
  title     = {Global-Local Regularization Via Distributional Robustness},
  year      = {2022},
  keywords  = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  publisher = {arXiv},
}

@Misc{le2021labelshift,
  author    = {Le, T. and Do, D. and Nguyen, T. and Nguyen, H. and Bui, H. and Ho, N. and Phung, D.},
  title     = {On Label Shift in Domain Adaptation via Wasserstein Distance},
  year      = {2021},
  keywords  = {Machine Learning (cs.LG), Methodology (stat.ME), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  publisher = {arXiv},
}

@InProceedings{phung2021on,
  author    = {T. Q. Phung and T. Le and L. T. Vuong and T. Tran and A. T. Tran and H. Bui and D. Phung},
  booktitle = {Advances in Neural Information Processing Systems},
  title     = {On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources},
  year      = {2021},
}

@Comment{jabref-meta: databaseType:bibtex;}
