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Machine learning models have been found to learn shortcuts -- unintended decision rules that are unable to generalize -- undermining models' reliability.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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The Nature of Statistical Learning Theory
Vladimir Vapnik · 1999
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Ensemble Methods in Machine Learning
Thomas G. Dietterich · 2000
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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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3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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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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Identity Mappings in Deep Residual Networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Improved Regularization of Convolutional Neural Networks with Cutout
Terrance DeVries and Graham W Taylor · 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 · 2018
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Places: A 10 Million Image Database for Scene Recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 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, Josh Tenenbaum, and Boris Katz · 2019
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Does object recognition work for everyone?
Terrance de Vries, Ishan Misra, Changhan Wang, and Laurens van der Maaten · 2019
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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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LVIS: A Dataset for Large Vocabulary Instance Segmentation
Agrim Gupta, Piotr Dollár, and Ross Girshick · 2019
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Panoptic Segmentation
Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollar · 2019
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Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Learning Robust Global Representations by Penalizing Local Predictive Power
Haohan Wang, Songwei Ge, Eric P. Xing, and Zachary C. Lipton · 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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CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 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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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Big Transfer (BiT): General Visual Representation Learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 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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Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
Xavier Soria Poma, Edgar Riba, and Angel Sappa · 2020
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Designing Network Design Spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang · 2020
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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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Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation
Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky · 2020
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Random Erasing Data Augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2020
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Systematic generalisation with group invariant predictions
Faruk Ahmed, Yoshua Bengio, Harm van Seijen, and Aaron Courville · 2021
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Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
Yujia Bao, Shiyu Chang, and Regina Barzilay · 2021
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Towards Robust Classification Model by Counterfactual and Invariant Data Generation
Chun-Hao Chang, George Alexandru Adam, and Anna Goldenberg · 2021
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An Empirical Study of Training Self-Supervised Vision Transformers
The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models
Greg d’Eon, Jason d’Eon, James R. Wright, and Kevin Leyton-Brown · 2022
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Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Sabri Eyuboglu, Maya Varma, Khaled Kamal Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, and Christopher Re · 2022
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Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, and Ludwig Schmidt · 2022
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Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Mannat Singh, Ishan Misra, Levent Sagun, Armand Joulin, and Piotr Bojanowski · 2022
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Masked Autoencoders Are Scalable Vision Learners
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Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Per-Pixel Classification is Not All You Need for Semantic Segmentation
Bowen Cheng, Alexander G. Schwing, and Alexander Kirillov · 2021
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Environment Inference for Invariant Learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 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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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 · 2021
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DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities
Elias Eulig, Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Kilian Rambach, William Beluch, Xiahan Shi, and Volker Fischer · 2021
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Towards Non-I.I.D. image classification: A dataset and baselines
Yue He, Zheyan Shen, and Peng Cui · 2021
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The Robustness Limits of SoTA Vision Models to Natural Variation
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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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On Feature Learning in the Presence of Spurious Correlations
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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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3DB: A Framework for Debugging Computer Vision Models
Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, Logan Engstrom, Vibhav Vineet, Kai Yuanqing Xiao, Pengchuan Zhang, Shibani Santurkar, Greg Yang, Ashish Kapoor, and Aleksander Madry · 2022
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Discover and Mitigate Unknown Biases with Debiasing Alternate Networks
Zhiheng Li, Anthony Hoogs, and Chenliang Xu · 2022
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MetaShift: A Dataset of Datasets for Evaluating Contextual Distribution Shifts and Training Conflicts
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ZIN: When and How to Learn Invariance Without Environment Partition?
Yong Lin, Shengyu Zhu, Lu Tan, and Peng Cui · 2022
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Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
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AugLy: Data Augmentations for Robustness
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RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness
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The dollar street dataset: Images representing the geographic and socioeconomic diversity of the world
William A Gaviria Rojas, Sudnya Diamos, Keertan Ranjan Kini, David Kanter, Vijay Janapa Reddi, and Cody Coleman · 2022
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ImageNet-D: A new challenging robustness dataset inspired by domain adaptation
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If your data distribution shifts, use self-learning
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LAION-5B: An open large-scale dataset for training next generation image-text models
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Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
Luca Scimeca, Seong Joon Oh, Sanghyuk Chun, Michael Poli, and Sangdoo Yun · 2022
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Unsupervised Learning of Debiased Representations With Pseudo-Attributes
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An Investigation of Critical Issues in Bias Mitigation Techniques
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Revisiting Weakly Supervised Pre-Training of Visual Perception Models
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Salient ImageNet: How to discover spurious features in Deep Learning?
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Model soups: Averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and Ludwig Schmidt · 2022
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Robust fine-tuning of zero-shot models
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Rich Feature Construction for the Optimization-Generalization Dilemma
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Scaling Fair Learning to Hundreds of Intersectional Groups
Eric Zhao, De-An Huang, Hao Liu, Zhiding Yu, Anqi Liu, Olga Russakovsky, and Anima Anandkumar · 2022
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ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, and Mark Ibrahim · 2023
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Distilling Model Failures as Directions in Latent Space
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Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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