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Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels.
Learning from imbalanced data
Haibo He and Edwardo A. Garcia · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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High-performance neural networks for visual object classification
Dan C Cireşan, Ueli Meier, Jonathan Masci, Luca M Gambardella, and Jürgen Schmidhuber · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge J. Belongie · 2011
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning deep representation for imbalanced classification
Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang · 2016
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith · 2017
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Cost-sensitive learning of deep feature representations from imbalanced data
Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, and Roberto Togneri · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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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
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What is the effect of importance weighting in deep learning?
Jonathon Byrd and Zachary Lipton · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Variance-based regularization with convex objectives
John Duchi and Hongseok Namkoong · 2019
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Distributionally robust losses against mixture covariate shifts
John C Duchi, Tatsunori Hashimoto, and Hongseok Namkoong · 2019
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Survey on deep learning with class imbalance
Justin M Johnson and Taghi M Khoshgoftaar · 2019
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Maximum weighted loss discrepancy
Fereshte Khani, Aditi Raghunathan, and Percy Liang · 2019
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Distributionally robust language modeling
Yonatan Oren, Shiori Sagawa, Tatsunori B Hashimoto, and Percy Liang · 2019
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré · 2020
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Data imbalance in classification: Experimental evaluation
Fadi Thabtah, Suhel Hammoud, Firuz Kamalov, and Amanda Gonsalves · 2020
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Towards debiasing nlu models from unknown biases
Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych · 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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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
Cited alongside, same era.
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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Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations
Michael Zhang, Nimit S Sohoni, Hongyang R Zhang, Chelsea Finn, and Christopher Ré · 2019
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Heteroskedastic and imbalanced deep learning with adaptive regularization
Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, and Tengyu Ma · 2020
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Shortcut learning in deep neural networks
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann · 2020
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Model patching: Closing the subgroup performance gap with data augmentation
Karan Goel, Albert Gu, Yixuan Li, and Christopher Ré · 2020
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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, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 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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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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Robust representation learning via perceptual similarity metrics
Saeid A Taghanaki, Kristy Choi, Amir Hosein Khasahmadi, and Anirudh Goyal · 2021
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Selecmix: Debiased learning by contradicting-pair sampling
Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak, Seong Joon Oh, Damien Teney, Jin-Hwa Kim, and Byoung-Tak Zhang · 2022
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Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz · 2022
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A conservative approach for unbiased learning on unknown biases
Myeongho Jeon, Daekyung Kim, Woochul Lee, Myungjoo Kang, and Joonseok Lee · 2022
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Learning debiased classifier with biased committee
Nayeong Kim, Sehyun Hwang, Sungsoo Ahn, Jaesik Park, and Suha Kwak · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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Diversify and disambiguate: Learning from underspecified data
Yoonho Lee, Huaxiu Yao, and Chelsea Finn · 2022
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
Junhyun Nam, Jaehyung Kim, Jaeho Lee, and Jinwoo Shin · 2022
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Unsupervised learning of debiased representations with pseudo-attributes
Seonguk Seo, Joon-Young Lee, and Bohyung Han · 2022
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Improving out-of-distribution robustness via selective augmentation
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn · 2022
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