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month = {June},
year = {2018}
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author = {Kreiss, Sven and Bertoni, Lorenzo and Alahi, Alexandre},
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@misc{tian2019directpose,
  doi = {10.48550/ARXIV.1911.07451},
  url = {https://arxiv.org/abs/1911.07451},
  author = {Tian, Zhi and Chen, Hao and Shen, Chunhua},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {DirectPose: Direct End-to-End Multi-Person Pose Estimation},
  publisher = {arXiv},
  year = {2019},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

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    title     = {Bottom-Up Human Pose Estimation via Disentangled Keypoint Regression},
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    month     = {June},
    year      = {2021},
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}

@inproceedings{SunXLW19,
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          Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao},
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    year      = {2021},
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}

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@inproceedings{shaw2019squeezenas,
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@article{chen2019fasterseg,
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  year={2019}
}

@inproceedings{liu2019auto,
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  year={2019}
}

@inproceedings{cordts2016cityscapes,
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  year={2016}
}

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  year={2015}
}

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@misc{hosseini2020learning,
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      author={Ramtin Hosseini and Pengtao Xie},
      year={2020},
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      primaryClass={cs.LG}
}

@article{liu2018darts,
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}

@article{krizhevsky2017imagenet,
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}

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@article{van2017inaturalist,
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  year={2017}
}

@misc{zoph2018learning,
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      author={Barret Zoph and Vijay Vasudevan and Jonathon Shlens and Quoc V. Le},
      year={2018},
      eprint={1707.07012},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@inproceedings{10.5555/2999134.2999257,
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
title = {ImageNet Classification with Deep Convolutional Neural Networks},
year = {2012},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.},
booktitle = {Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1},
pages = {1097–1105},
numpages = {9},
location = {Lake Tahoe, Nevada},
series = {NIPS'12}
}

@article{CIFAR-10,
title= {CIFAR-10 (Canadian Institute for Advanced Research)},
journal= {},
author= {Alex Krizhevsky and Vinod Nair and Geoffrey Hinton},
year= {},
url= {http://www.cs.toronto.edu/~kriz/cifar.html},
abstract= {The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. 

The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class. },
keywords= {Dataset},
terms= {}
}

@article{CIFAR-100,
title= {CIFAR100},
author= {Krizhevsky, 2009},
abstract= {This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a “fine” label (the class to which it belongs) and a “coarse” label (the superclass to which it belongs).},
keywords= {fastai},
terms= {},
license= {},
superseded= {},
url= {}
}

@article{krizhevsky2012imagenet,
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}

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	year={2020},
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}

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}

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  year={2018}
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