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Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability.
The effective rank: A measure of effective dimensionality
Olivier Roy and Martin Vetterli · 2007
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The caltech-ucsd birds-200-2011 dataset
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Deep learning face attributes in the wild
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Colorful image colorization
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Robustness may be at odds with accuracy
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Implicit regularization in deep matrix factorization
Sanjeev Arora, Nadav Cohen, Wei Hu, and Yuping Luo · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng · 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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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Learning robust representations by projecting superficial statistics out
Haohan Wang, Zexue He, Zachary C Lipton, and Eric P Xing · 2019
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Increasing robustness to spurious correlations using forgettable examples
Yadollah Yaghoobzadeh, Soroush Mehri, Remi Tachet, Timothy J Hazen, and Alessandro Sordoni · 2019
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Learning de-biased representations with biased representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, and Seong Joon Oh · 2020
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Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos · 2020
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A simple framework for contrastive learning of visual representations
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An image is worth 16x16 words: Transformers for image recognition at scale
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On the surprising similarities between supervised and self-supervised models
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2020
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Large-scale methods for distributionally robust optimization
Unbiased classification through bias-contrastive and bias-balanced learning
Youngkyu Hong and Eunho Yang · 2021
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The low-rank simplicity bias in deep networks
Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, and Phillip Isola · 2021
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Understanding dimensional collapse in contrastive self-supervised learning
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian · 2021
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Explaining in style: Training a gan to explain a classifier in stylespace
Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T Freeman, Phillip Isola, Amir Globerson, Michal Irani, et al · 2021
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Learning debiased representation via disentangled feature augmentation
Jungsoo Lee, Eungyeup Kim, Juyoung Lee, Jihyeon Lee, and Jaegul Choo · 2021
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Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 2020
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Understanding the failure modes of out-of-distribution generalization
Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur · 2020
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Learning from failure: De-biasing classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
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Implicit regularization in deep learning may not be explainable by norms
Noam Razin and Nadav Cohen · 2020
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Contrastive learning with hard negative samples
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An investigation of why overparameterization exacerbates spurious correlations
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Towards fairness in visual recognition: Effective strategies for bias mitigation
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Discover the unknown biased attribute of an image classifier
Zhiheng Li and Chenliang Xu · 2021
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Just train twice: Improving group robustness without training group information
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Can contrastive learning avoid shortcut solutions?
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End: Entangling and disentangling deep representations for bias correction
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Understanding the behaviour of contrastive loss
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Barlow twins: Self-supervised learning via redundancy reduction
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Can subnetwork structure be the key to out-of-distribution generalization?
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Contrastive learning inverts the data generating process
Roland S Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2021
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Simple data balancing achieves competitive worst-group-accuracy
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Last layer re-training is sufficient for robustness to spurious correlations
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Discover and mitigate unknown biases with debiasing alternate networks
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Hard negative sampling strategies for contrastive representation learning
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Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations
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Partition-and-debias: Agnostic biases mitigation via a mixture of biases-specific experts
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Training debiased subnetworks with contrastive weight pruning
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