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Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions.
Gradient-based learning applied to document recognition
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Generalizing from several related classification tasks to a new unlabeled sample
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
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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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The caltech-ucsd birds-200-2011 dataset
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Cnn features off-the-shelf: an astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Fast r-cnn
Ross Girshick · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
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Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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Jupyter notebooks – a publishing format for reproducible computational workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica Hamrick, Jason Grout, Sylvain Corlay, Paul Ivanov, Damián Avila, Safia Abdalla, and Carol Willing · 2016
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Improving weakly-supervised object localization by micro-annotation
Alexander Kolesnikov and Christoph H Lampert · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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A reductions approach to fair classification
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach · 2018
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 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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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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Excessive invariance causes adversarial vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, and Matthias Bethge · 2018
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Exploring the limits of weakly supervised pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens Van Der Maaten · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Amir Rosenfeld, Richard Zemel, and John K Tsotsos · 2018
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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann · 2018
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Michael A Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh Nguyen · 2019
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
Towards debiasing nlu models from unknown biases
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Noise or signal: The role of image backgrounds in object recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Coping with label shift via distributionally robust optimisation
Jingzhao Zhang, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, and Suvrit Sra · 2020
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
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Rubi: Reducing unimodal biases for visual question answering
Remi Cadene, Corentin Dancette, Matthieu Cord, Devi Parikh, et al · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2019
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Maximum weighted loss discrepancy
Fereshte Khani, Aditi Raghunathan, and Percy Liang · 2019
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Learning not to learn: Training deep neural networks with biased data
Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, and Junmo Kim · 2019
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Exploring the limits of large scale pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, and Hanie Sedghi · 2021
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Linear unit-tests for invariance discovery
Benjamin Aubin, Agnieszka Słowik, Martin Arjovsky, Leon Bottou, and David Lopez-Paz · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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A too-good-to-be-true prior to reduce shortcut reliance
Nikolay Dagaev, Brett D Roads, Xiaoliang Luo, Daniel N Barry, Kaustubh R Patil, and Bradley C Love · 2021
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Partial success in closing the gap between human and machine vision
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2021
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Pytorch library for cam methods
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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 · 2021
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Simple data balancing achieves competitive worst-group-accuracy
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Shape or texture: Understanding discriminative features in cnns
Md Amirul Islam, Matthew Kowal, Patrick Esser, Sen Jia, Bjorn Ommer, Konstantinos G Derpanis, and Neil Bruce · 2021
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Explaining the efficacy of counterfactually augmented data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C Lipton · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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Fast adaptation with linearized neural networks
Wesley Maddox, Shuai Tang, Pablo Moreno, Andrew Gordon Wilson, and Andreas Damianou · 2021
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Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C Courville, Doina Precup, and Guillaume Lajoie · 2021
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Optimal representations for covariate shift
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Extending the wilds benchmark for unsupervised adaptation
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Which shortcut cues will dnns choose? a study from the parameter-space perspective
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Salient imagenet: How to discover spurious features in deep learning?
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Understanding failures of deep networks via robust feature extraction
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Barack: Partially supervised group robustness with guarantees
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End: Entangling and disentangling deep representations for bias correction
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Counterfactual invariance to spurious correlations: Why and how to pass stress tests
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Learning bias-invariant representation by cross-sample mutual information minimization
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Informativeness and invariance: Two perspectives on spurious correlations in natural language
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