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% ----- introduction
@article{raina2003classification,
	title={Classification with hybrid generative/discriminative models},
	author={Raina, Rajat and Shen, Yirong and Mccallum, Andrew and Ng, Andrew},
	journal={Advances in neural information processing systems},
	volume={16},
	year={2003}
}

@inproceedings{fujino2005hybrid,
	title={A hybrid generative/discriminative approach to semi-supervised classifier design},
	author={Fujino, Akinori and Ueda, Naonori and Saito, Kazumi},
	booktitle={Proceedings of the National Conference on Artificial Intelligence},
	volume={20},
	number={2},
	pages={764},
	year={2005},
	organization={Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999}
}

@article{bosch2008scene,
	title={Scene classification using a hybrid generative/discriminative approach},
	author={Bosch, Anna and Zisserman, Andrew and Munoz, Xavier},
	journal={IEEE transactions on pattern analysis and machine intelligence},
	volume={30},
	number={4},
	pages={712--727},
	year={2008},
	publisher={IEEE}
}

@article{ouyang2011indoor,
	title={Indoor location estimation with reduced calibration exploiting unlabeled data via hybrid generative/discriminative learning},
	author={Ouyang, Robin Wentao and Wong, Albert Kai-Sun and Lea, Chin-Tau and Chiang, Mung},
	journal={IEEE transactions on mobile computing},
	volume={11},
	number={11},
	pages={1613--1626},
	year={2011},
	publisher={IEEE}
}

@inproceedings{kuleshov2017deep,
	title={Deep hybrid models: Bridging discriminative and generative approaches},
	author={Kuleshov, Volodymyr and Ermon, Stefano},
	booktitle={Proceedings of the Conference on Uncertainty in AI (UAI)},
	year={2017}
}

@article{roth2018hybrid,
	title={Hybrid generative-discriminative training of Gaussian mixture models},
	author={Roth, Wolfgang and Peharz, Robert and Tschiatschek, Sebastian and Pernkopf, Franz},
	journal={Pattern recognition letters},
	volume={112},
	pages={131--137},
	year={2018},
	publisher={Elsevier}
}

@inproceedings{grcic2022densehybrid,
	title={Densehybrid: Hybrid anomaly detection for dense open-set recognition},
	author={Grci{\'c}, Matej and Bevandi{\'c}, Petra and {\v{S}}egvi{\'c}, Sini{\v{s}}a},
	booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXV},
	pages={500--517},
	year={2022},
	organization={Springer}
}

@inproceedings{loh2022long,
	title={Long-term Action Forecasting Using Multi-headed Attention-based Variational Recurrent Neural Networks},
	author={Loh, Siyuan Brandon and Roy, Debaditya and Fernando, Basura},
	booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages={2419--2427},
	year={2022}
}

@inproceedings{cao2022deep,
	title={Deep Hybrid Models for Out-of-Distribution Detection},
	author={Cao, Senqi and Zhang, Zhongfei},
	booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages={4733--4743},
	year={2022}
}

@article{xian2018zero,
	title={Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly},
	author={Xian, Yongqin and Lampert, Christoph H and Schiele, Bernt and Akata, Zeynep},
	journal={IEEE transactions on pattern analysis and machine intelligence},
	volume={41},
	number={9},
	pages={2251--2265},
	year={2018},
	publisher={IEEE}
}

@article{mancini2022learning,
	title={Learning graph embeddings for open world compositional zero-shot learning},
	author={Mancini, Massimiliano and Naeem, Muhammad Ferjad and Xian, Yongqin and Akata, Zeynep},
	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
	year={2022},
	publisher={IEEE}
}

% ----- Related work
% -- Existing PCB Soldering Defect Detection Methods
@article{wu2022pcbnet,
	title={PCBNet: A Lightweight Convolutional Neural Network for Defect Inspection in Surface Mount Technology},
	author={Wu, Hongjin and Lei, Ruoshan and Peng, Yibing},
	journal={IEEE Transactions on Instrumentation and Measurement},
	volume={71},
	pages={1--14},
	year={2022},
	publisher={IEEE}
}

@article{dai2020soldering,
	title={Soldering defect detection in automatic optical inspection},
	author={Dai, Wenting and Mujeeb, Abdul and Erdt, Marius and Sourin, Alexei},
	journal={Advanced Engineering Informatics},
	volume={43},
	pages={101004},
	year={2020},
	publisher={Elsevier}
}

@article{liao2022solder,
	title={Solder Joint Defect Inspection Method Based on ConvNeXt-YOLOX},
	author={Liao, Shuaidong and Huang, Chunyue and Liang, Ying and Zhang, Huaiquan and Liu, Shoufu},
	journal={IEEE Transactions on Components, Packaging and Manufacturing Technology},
	volume={12},
	number={11},
	pages={1890--1898},
	year={2022},
	publisher={IEEE}
}

@article{bhattacharya2022end,
	title={End-to-end deep learning framework for printed circuit board manufacturing defect classification},
	author={Bhattacharya, Abhiroop and Cloutier, Sylvain G},
	journal={Scientific Reports},
	volume={12},
	number={1},
	pages={12559},
	year={2022},
	publisher={Nature Publishing Group UK London}
}

@article{vaswani2017attention,
	title={Attention is all you need},
	author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
	journal={Advances in neural information processing systems},
	volume={30},
	year={2017}
}

@article{cai2018smt,
	title={SMT solder joint inspection via a novel cascaded convolutional neural network},
	author={Cai, Nian and Cen, Guandong and Wu, Jixiu and Li, Feiyang and Wang, Han and Chen, Xindu},
	journal={IEEE Transactions on Components, Packaging and Manufacturing Technology},
	volume={8},
	number={4},
	pages={670--677},
	year={2018},
	publisher={IEEE}
}

@article{ulger2021anomaly,
	title={Anomaly detection for solder joints using $\beta$-VAE},
	author={Ulger, Furkan and Yuksel, Seniha Esen and Yilmaz, Atila},
	journal={IEEE Transactions on Components, Packaging and Manufacturing Technology},
	volume={11},
	number={12},
	pages={2214--2221},
	year={2021},
	publisher={IEEE}
}

% -- Unknown Awareness in Defect Detection
@article{cheon2019convolutional,
	title={Convolutional neural network for wafer surface defect classification and the detection of unknown defect class},
	author={Cheon, Sejune and Lee, Hankang and Kim, Chang Ouk and Lee, Seok Hyung},
	journal={IEEE Transactions on Semiconductor Manufacturing},
	volume={32},
	number={2},
	pages={163--170},
	year={2019},
	publisher={IEEE}
}

@article{habibpour2021uncertainty,
	title={An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products},
	author={Habibpour, Maryam and Gharoun, Hassan and Tajally, AmirReza and Shamsi, Afshar and Asgharnezhad, Hamzeh and Khosravi, Abbas and Nahavandi, Saeid},
	journal={arXiv preprint arXiv:2107.11643},
	year={2021}
}

@article{zhou2021semi,
	title={Semi-supervised fabric defect detection based on image reconstruction and density estimation},
	author={Zhou, Qihong and Mei, Jun and Zhang, Qian and Wang, Shaozong and Chen, Ge},
	journal={Textile Research Journal},
	volume={91},
	number={9-10},
	pages={962--972},
	year={2021},
	publisher={SAGE Publications Sage UK: London, England}
}

% -- Compositional Zero-Shot Learning
@inproceedings{misra2017red,
	title={From red wine to red tomato: Composition with context},
	author={Misra, Ishan and Gupta, Abhinav and Hebert, Martial},
	booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
	pages={1792--1801},
	year={2017}
}

@inproceedings{purushwalkam2019task,
	title={Task-driven modular networks for zero-shot compositional learning},
	author={Purushwalkam, Senthil and Nickel, Maximilian and Gupta, Abhinav and Ranzato, Marc'Aurelio},
	booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
	pages={3593--3602},
	year={2019}
}


@inproceedings{li2020symmetry,
	title={Symmetry and group in attribute-object compositions},
	author={Li, Yong-Lu and Xu, Yue and Mao, Xiaohan and Lu, Cewu},
	booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
	pages={11316--11325},
	year={2020}
}

% -------- Experiment and Results
@article{van2008visualizing,
	title={Visualizing data using t-SNE.},
	author={Van der Maaten, Laurens and Hinton, Geoffrey},
	journal={Journal of machine learning research},
	volume={9},
	number={11},
	year={2008}
}

% ---------------------- 
@article{vgg,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}

@article{resnet,
  title={Deep residual learning for image recognition. arXiv. 2015. doi: 10.48550},
  author={He, K and Zhang, XY and Ren, SQ and Sun, J},
  journal={arXiv preprint arXiv.1512.03385},
  year={2015},
}

@inproceedings{densenet,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}

@inproceedings{mobilenetv3,
  title={Searching for mobilenetv3},
  author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1314--1324},
  year={2019}
}

@article{sohn2020fixmatch,
  title={Fixmatch: Simplifying semi-supervised learning with consistency and confidence},
  author={Sohn, Kihyuk and Berthelot, David and Carlini, Nicholas and Zhang, Zizhao and Zhang, Han and Raffel, Colin A and Cubuk, Ekin Dogus and Kurakin, Alexey and Li, Chun-Liang},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={596--608},
  year={2020}
}

@article{cutout,
  title={Improved regularization of convolutional neural networks with cutout},
  author={DeVries, Terrance and Taylor, Graham W},
  journal={arXiv preprint arXiv:1708.04552},
  year={2017}
}

@article{berthelot2019remixmatch,
  title={Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring},
  author={Berthelot, David and Carlini, Nicholas and Cubuk, Ekin D and Kurakin, Alex and Sohn, Kihyuk and Zhang, Han and Raffel, Colin},
  journal={arXiv preprint arXiv:1911.09785},
  year={2019}
}

@inproceedings{cubuk2020randaugment,
  title={Randaugment: Practical automated data augmentation with a reduced search space},
  author={Cubuk, Ekin D and Zoph, Barret and Shlens, Jonathon and Le, Quoc V},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={702--703},
  year={2020}
}

@inproceedings{qian2019softtriple,
  title={Softtriple loss: Deep metric learning without triplet sampling},
  author={Qian, Qi and Shang, Lei and Sun, Baigui and Hu, Juhua and Li, Hao and Jin, Rong},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6450--6458},
  year={2019}
}

@article{sensoy2018evidential,
  title={Evidential deep learning to quantify classification uncertainty},
  author={Sensoy, Murat and Kaplan, Lance and Kandemir, Melih},
  journal={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}


@inproceedings{zhang2021defect,
  title={Defect-GAN: High-fidelity defect synthesis for automated defect inspection},
  author={Zhang, Gongjie and Cui, Kaiwen and Hung, Tzu-Yi and Lu, Shijian},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2524--2534},
  year={2021}
}

@book{kotz2004continuous,
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@article{khosla2020supervised,
  title={Supervised contrastive learning},
  author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={18661--18673},
  year={2020}
}

@inproceedings{SENet,
  title={Squeeze-and-excitation networks},
  author={Hu, Jie and Shen, Li and Sun, Gang},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7132--7141},
  year={2018}
}

@article{habibpour2021uncertainty,
  title={An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products},
  author={Habibpour, Maryam and Gharoun, Hassan and Tajally, AmirReza and Shamsi, Afshar and Asgharnezhad, Hamzeh and Khosravi, Abbas and Nahavandi, Saeid},
  journal={arXiv preprint arXiv:2107.11643},
  year={2021}
}

@inproceedings{simclr,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}

@article{simclrv2,
  title={Big self-supervised models are strong semi-supervised learners},
  author={Chen, Ting and Kornblith, Simon and Swersky, Kevin and Norouzi, Mohammad and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={22243--22255},
  year={2020}
}

@inproceedings{moco,
  author    = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross B. Girshick},
  title     = {Momentum Contrast for Unsupervised Visual Representation Learning},
  booktitle = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2020, Seattle, WA, USA, June 13-19, 2020},
  pages     = {9726-9735},
  publisher = {Computer Vision Foundation / {IEEE}},
  year      = {2020},
  url       = {https://doi.org/10.1109/CVPR42600.2020.00975},
  doi       = {10.1109/CVPR42600.2020.00975},
  timestamp = {Tue, 31 Aug 2021 14:00:04 +0200},
  biburl    = {https://dblp.org/rec/conf/cvpr/He0WXG20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{mocov2,
  title   = {Improved Baselines with Momentum Contrastive Learning},
  author  = {Xinlei Chen and Haoqi Fan and Ross Girshick and Kaiming He},
  year    = {2020},
  journal = {arXiv preprint arXiv: Arxiv-2003.04297}
}

@inproceedings{mocov3,
  title={An empirical study of training self-supervised vision transformers},
  author={Chen, Xinlei and Xie, Saining and He, Kaiming},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9640--9649},
  year={2021}
}

@article{vit,
  title={An image is worth 16x16 words: Transformers for image recognition at scale},
  author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and others},
  journal={arXiv preprint arXiv:2010.11929},
  year={2020}
}

@article{byol,
  title={Bootstrap your own latent-a new approach to self-supervised learning},
  author={Grill, Jean-Bastien and Strub, Florian and Altch{\'e}, Florent and Tallec, Corentin and Richemond, Pierre and Buchatskaya, Elena and Doersch, Carl and Avila Pires, Bernardo and Guo, Zhaohan and Gheshlaghi Azar, Mohammad and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={21271--21284},
  year={2020}
}

@inproceedings{simsiam,
  author    = {Xinlei Chen and Kaiming He},
  title     = {Exploring Simple Siamese Representation Learning},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2021, virtual, June 19-25, 2021},
  pages     = {15750-15758},
  publisher = {Computer Vision Foundation / {IEEE}},
  year      = {2021},
  url       = {https://openaccess.thecvf.com/content/CVPR2021/html/Chen\_Exploring\_Simple\_Siamese\_Representation\_Learning\_CVPR\_2021\_paper.html},
  doi       = {10.1109/CVPR46437.2021.01549},
  timestamp = {Mon, 18 Jul 2022 16:47:41 +0200},
  biburl    = {https://dblp.org/rec/conf/cvpr/ChenH21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{paszke2017automatic,
  title={Automatic differentiation in pytorch},
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  year={2017}
}

@inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
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@inproceedings{ioffe2015batch,
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@article{fukushima1975cognitron,
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@inproceedings{zakaria2020automated,
  title={Automated detection of printed circuit boards (PCB) defects by using machine learning in electronic manufacturing: Current approaches},
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  booktitle={IOP Conference Series: Materials Science and Engineering},
  volume={767},
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  year={2020},
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@inproceedings{deng2018building,
  title={Building an automatic defect verification system using deep neural network for pcb defect classification},
  author={Deng, Yu-Shan and Luo, An-Chun and Dai, Min-Ji},
  booktitle={2018 4th International Conference on Frontiers of Signal Processing (ICFSP)},
  pages={145--149},
  year={2018},
  organization={IEEE}
}

@inproceedings{tai2021pcb,
  title={A PCB Soldering Joint Defect Recognition System Using Convolutional Neural Network},
  author={Tai, Kar Wai and Chuah, Joon Huang and Leong, Hungyang and Kamarudin, Nazhatul Hafizah},
  booktitle={2021 Innovations in Power and Advanced Computing Technologies (i-PACT)},
  pages={1--6},
  year={2021},
  organization={IEEE}
}

@inproceedings{aggarwal2022survey,
  title={A Survey on Automatic Printed Circuit Board Defect Detection Techniques},
  author={Aggarwal, Nikhil and Deshwal, Manish and Samant, Piyush},
  booktitle={2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)},
  pages={853--856},
  year={2022},
  organization={IEEE}
}

@inproceedings{hsu2021upon,
  title={Upon Human's Unknown, Can AI Help to Address Uncertainty? A Practice on Package Substrate AOI Defect Detection},
  author={Hsu, Yi-Chung and Wang, Jheng-Ting and Huang, Wong-Shian},
  booktitle={2021 16th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT)},
  pages={42--45},
  year={2021},
  organization={IEEE}
}

@inproceedings{wang2019multi,
  title={Multi-similarity loss with general pair weighting for deep metric learning},
  author={Wang, Xun and Han, Xintong and Huang, Weilin and Dong, Dengke and Scott, Matthew R},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5022--5030},
  year={2019}
}

@inproceedings{movshovitz2017no,
  title={No fuss distance metric learning using proxies},
  author={Movshovitz-Attias, Yair and Toshev, Alexander and Leung, Thomas K and Ioffe, Sergey and Singh, Saurabh},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={360--368},
  year={2017}
}

@inproceedings{hadsell2006dimensionality,
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@inproceedings{schroff2015facenet,
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@inproceedings{li2022siamese,
  title={Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning},
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  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9326--9335},
  year={2022}
}



@article{xian2018zero,
  title={Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly},
  author={Xian, Yongqin and Lampert, Christoph H and Schiele, Bernt and Akata, Zeynep},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={41},
  number={9},
  pages={2251--2265},
  year={2018},
  publisher={IEEE}
}

@Misc{Bayesian,
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}

@inproceedings{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  booktitle={International conference on machine learning},
  pages={1597--1607},
  year={2020},
  organization={PMLR}
}