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We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues.
Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Learning Deep Features for Discriminative Localization
B. Zhou, A. Khosla, Lapedriza. A., A. Oliva, and A. Torralba · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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Age progression/regression by conditional adversarial autoencoder
Zhifei Zhang, Yang Song, and Hairong Qi · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 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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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Amir Rosenfeld, Richard S. 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 Beardsworth 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 M Nguyen · 2019
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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Joshua B. Tenenbaum, and Boris Katz · 2019
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This looks like that: deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang · 2019
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Image synthesis with a single (robust) classifier, 2019
Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Andrew Ilyas, Logan Engstrom, and Aleksander Madry · 2019
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Not using the car to see the sidewalk — quantifying and controlling the effects of context in classification and segmentation
Rakshith Shetty, Bernt Schiele, and Mario Fritz · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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Pytorch image models
Ross Wightman · 2019
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Invariant risk minimization, 2020
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2020
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Lucas Beyer, Olivier J. H’enaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
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Learning from failure: Training debiased classifier from biased classifier
Jun Hyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Understanding failures of deep networks via robust feature extraction
Sahil Singla, Besmira Nushi, S. Shah, Ece Kamar, and Eric Horvitz · 2021
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Leveraging sparse linear layers for debuggable deep networks
Eric Wong, Shibani Santurkar, and Aleksander Madry · 2021
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Noise or signal: The role of image backgrounds in object recognition
Kai Yuanqing Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2021
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Ood-bench: Benchmarking and understanding out-of-distribution generalization datasets and algorithms
Nanyang Ye, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, and Zhenguo Li · 2021
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Re-labeling imagenet: from single to multi-labels, from global to localized labels
Sangdoo Yun, Seong Joon Oh, Byeongho Heo, Dongyoon Han, Junsuk Choe, and Sanghyuk Chun · 2021
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Nimit Sharad Sohoni, Jared A. Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré · 2020
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Influence functions in deep learning are fragile
Samyadeep Basu, Philip Pope, and Soheil Feizi · 2021
Cited alongside, same era.
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen*, Saining Xie*, and Kaiming He · 2021
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Explaining latent representations with a corpus of examples
Jonathan Crabbe, Zhaozhi Qian, Fergus Imrie, and Mihaela van der Schaar · 2021
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Ai for radiographic covid-19 detection selects shortcuts over signal
Alex J. DeGrave, Joseph D. Janizek, and Su-In Lee · 2021
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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 I. Moldovan, Sylvan Gelly, Neil Houlsby, Xiaohua Zhai, and Mario Lucic · 2021
Cited alongside, same era.
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 Lixuan Zhu, Samyak Parajuli, Mike Guo, Dawn Xiaodong Song, Jacob Steinhardt, and Justin Gilmer · 2021
Cited alongside, same era.
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Coping with label shift via distributionally robust optimisation
J. Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, and Suvrit Sra · 2021
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Harmonizing the object recognition strategies of deep neural networks with humans
Thomas Fel, Ivan Felipe, Drew Linsley, and Thomas Serre · 2022
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Dissect: Disentangled simultaneous explanations via concept traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, and Rosalind W. Picard · 2022
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Natural language descriptions of deep visual features
Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili, Antonio Torralba, and Jacob Andreas · 2022
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Datamodels: Predicting predictions from training data
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
P. Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes
Mazda Moayeri, Phillip E. Pope, Yogesh Balaji, and Soheil Feizi · 2022
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
Jun Hyun Nam, Jaehyung Kim, Jaeho Lee, and Jinwoo Shin · 2022
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Salient imagenet: How to discover spurious features in deep learning?
Sahil Singla and Soheil Feizi · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed Huai hsin Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt · 2022
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Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations
Michael Zhang, Nimit Sharad Sohoni, Hongyang R. Zhang, Chelsea Finn, and Christopher R’e · 2022
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
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Text-to-concept (and back) via cross-model alignment
Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi, and Soheil Feizi · 2023
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