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Pretrained models from self-supervision are prevalently used in fine-tuning downstream tasks faster or for better accuracy.
Region filling and object removal by exemplar-based image inpainting
Antonio Criminisi, Patrick Pérez, and Kentaro Toyama · 2004
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A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2007
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Self-taught learning: transfer learning from unlabeled data
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y Ng · 2007
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T Freeman · 2008
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
Alexey Dosovitskiy, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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Magnet: a two-pronged defense against adversarial examples
Dongyu Meng and Hao Chen · 2017
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Extending defensive distillation
Nicolas Papernot and Patrick McDaniel · 2017
Cited alongside, same era.
Ensemble methods as a defense to adversarial perturbations against deep neural networks
Thilo Strauss, Markus Hanselmann, Andrej Junginger, and Holger Ulmer · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick McDaniel · 2017
Cited alongside, same era.
Feature squeezing: Detecting adversarial examples in deep neural networks
Weilin Xu, David Evans, and Yanjun Qi · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Testing robustness against unforeseen adversaries
Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, and Jacob Steinhardt · 2019
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Towards understanding the transferability of deep representations
Hong Liu, Mingsheng Long, Jianmin Wang, and Michael I Jordan · 2019
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Improving adversarial robustness via promoting ensemble diversity
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Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Domain generalization by solving jigsaw puzzles
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, and John C Duchi · 2019
Cited alongside, same era.
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
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Computer vision with a single (robust) classifier
Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, and Aleksander Madry · 2019
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Are labels required for improving adversarial robustness?
Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli, et al · 2019
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Selfie: Self-supervised pretraining for image embedding
Trieu H Trinh, Minh-Thang Luong, and Quoc V Le · 2019
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Towards a unified min-max framework for adversarial exploration and robustness, 2019
Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, and Bo Li · 2019
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Adversarially robust generalization just requires more unlabeled data
Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, and Liwei Wang · 2019
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
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Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference
Ting-Kuei Hu, Tianlong Chen, Haotao Wang, and Zhangyang Wang · 2020
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Self-supervised learning for generalizable out-of-distribution detection
Sina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang · 2020
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu · 2020
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