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For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments.
Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 1902
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Do ImageNet classifiers generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 1902
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David Tellez, Geert Litjens, Péter Bándi, Wouter Bulten, John-Melle Bokhorst, Francesco Ciompi, and Jeroen van der Laak · 1902
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 1903
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Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, and John Duchi · 1905
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EfficientNet: rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 1905
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Chhavi Yadav and Léon Bottou · 1905
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Do image classifiers generalize across time?, 2019
Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, and Ludwig Schmidt · 1906
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 1907
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R. T. McCoy, J. Min, and T. Linzen · 1911
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An elementary introduction to modern convex geometry
Keith Ball · 1997
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and Christopher Burges · 1998
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The OpenCV Library
G. Bradski · 2000
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Random forests
Leo Breiman · 2001
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Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2003
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The iWildCam 2020 competition dataset
Sara Beery, Elijah Cole, and Arvi Gjoka · 2004
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The effect of natural distribution shift on question answering models
John Miller, Karl Krauth, Benjamin Recht, and Ludwig Schmidt · 2004
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A survey on domain adaptation theory: learning bounds and theoretical guarantees, 2020
Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, and Younès Bennani · 2004
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X. Zhou, Y. Nie, H. Tan, and M. Bansal · 2004
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
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Vincent Lepetit and Pascal Fua · 2005
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Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2006
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
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The many faces of robustness: A critical analysis of out-of-distribution generalization, 2020
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2006
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John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman · 2007
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On robustness and transferability of convolutional neural networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D’Amour, Dan Moldovan, Sylvan Gelly, Neil Houlsby, Xiaohua Zhai, and Mario Lucic · 2007
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Ishaan Gulrajani and David Lopez-Paz · 2007
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A bayesian divergence prior for classiffier adaptation
Xiao Li and Jeff Bilmes · 2007
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Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2007
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Domain adaptation with multiple sources
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2008
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BREEDS: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2008
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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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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Multi-class AdaBoost
Trevor Hastie, Saharon Rosset, Ji Zhu, and Hui Zou · 2009
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Alex Krizhevsky · 2009
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EPnP: An accurate o(n) solution to the PnP problem
Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua · 2009
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Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
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Dataset Shift in Machine Learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2010
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Exploring simple siamese representation learning, 2020
Xinlei Chen and Kaiming He · 2011
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes
Yu Xiang, Tanner Schmidt, Venkatraman Narayanan, and Dieter Fox · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge
Peter Bandi, Oscar Geessink, Quirine Manson, Marcory van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, Quanzheng Li, Farhad Ghazvinian Zanjani, Svitlana Zinger, Keisuke Fukuta, Daisuke Komura, Vlado Ovtcharov, Shenghua Cheng, Shaoqun Zeng, Jeppe Thagaard, Anders B. Dahl, Huangjing Lin, Hao Chen, Ludwig Jacobsson, Martin Hedlund, Melih Çetin, Eren Halıcı, Hunter Jackson, Richard Chen, Fabian Both, Jörg Franke, Heidi Küsters-Vandevelde, Willem Vreuls, Peter Bult, Bram van Ginneken, Jeroen van der Laak, and Geert Litjens · 2018
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Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, and D. Sculley · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Out-distribution aware self-training in an open world setting, 2020
Maximilian Augustin and Matthias Hein · 2012
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Learning feature representations with k-means
Adam Coates and Andrew Y Ng · 2012
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Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes
Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Stefan Holzer, Gary Bradski, Kurt Konolige, and Nassir Navab · 2012
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WILDS: A benchmark of in-the-wild distribution shifts
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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CINIC-10 is not ImageNet or CIFAR-10, 2018
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Algorithms and theory for multiple-source adaptation
Judy Hoffman, Mehryar Mohri, and Ningshan Zhang · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
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ShuffleNet V2: practical guidelines for efficient CNN architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Certified defenses against adversarial examples
Aditi Raghunathan, Jacob Steinhardt, and Percy Liang · 2018
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Do CIFAR-10 classifiers generalize to CIFAR-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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MobileNetV2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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Integral human pose regression
Xiao Sun, Bin Xiao, Fangyin Wei, Shuang Liang, and Yichen Wei · 2018
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David Tellez, Maschenka Balkenhol, Irene Otte-Holler, Rob van de Loo, Rob Vogels, Peter Bult, Carla Wauters, Willem Vreuls, Suzanne Mol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, and Francesco Ciompi · 2018
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Deep object pose estimation for semantic robotic grasping of household objects
Jonathan Tremblay, Thang To, Balakumar Sundaralingam, Yu Xiang, Dieter Fox, and Stan Birchfield · 2018
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Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and Zico Kolter · 2018
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ShuffleNet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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UNet++: a nested U-Net architecture for medical image segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, and Jianming Liang · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V. Le · 2018
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ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
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Do better ImageNet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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RandAugment: practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2020
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Kosnet: A unified keypoint, orientation and scale network for probabilistic 6d pose estimation
Kunimatsu Hashimoto, Duy-Nguyen Ta, Eric Cousineau, and Russ Tedrake · 2020
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Harder or different? a closer look at distribution shift in dataset reproduction
Shangyun Lu, Bradley Nott, Aaron Olson, Alberto Todeschini, Hossein Vahabi, Yair Carmon, and Ludwig Schmidt · 2020
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The evolution of out-of-distribution robustness throughout fine-tuning, 2021
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