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A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features.
The effects of contextual scenes on the identification of objects
tephen E Palmer · 1975
Earlier work this paper cites.
Scene perception: Detecting and judging objects undergoing relational violations
Irving Biederman, Robert J Mezzanotte, and Jan C Rabinowitz · 1982
Earlier work this paper cites.
Contextual cueing: Implicit learning and memory of visual context guides spatial attention
Marvin M Chun and Yuhong Jiang · 1998
Earlier work this paper cites.
High-level scene perception
John M Henderson and Andrew Hollingworth · 1999
Earlier work this paper cites.
Contextual priming for object detection
Antonio Torralba · 2003
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
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Earlier work this paper cites.
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Earlier work this paper cites.
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Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Deep learning face attributes in the wild
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Earlier work this paper cites.
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Earlier work this paper cites.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
Earlier work this paper cites.
Revisiting visual question answering baselines
Allan Jabri, Armand Joulin, and Laurens Van Der Maaten · 2016
Earlier work this paper cites.
Unanimous prediction for 100% precision with application to learning semantic mappings
Fereshte Khani, Martin Rinard, and Percy Liang · 2016
Earlier work this paper cites.
A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
Wasserstein distributional robustness and regularization in statistical learning
Rui Gao, Xi Chen, and Anton J Kleywegt · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 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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Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
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The marginal value of adaptive gradient methods in machine learning
Ashia C Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, and Benjamin Recht · 2017
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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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Self-training avoids using spurious features under domain shift
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Khurram Javed, Martha White, and Yoshua Bengio · 2020
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Large-scale methods for distributionally robust optimization
Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 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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Recognition in terra incognita
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Tell me where to look: Guided attention inference network
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Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle-Perez, Chico Q Camargo, and Ard A Louis · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
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The pitfalls of simplicity bias in neural networks
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
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Score-cam: Score-weighted visual explanations for convolutional neural networks
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Noise or signal: The role of image backgrounds in object recognition
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Statistics of robust optimization: A generalized empirical likelihood approach
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Selective classification via one-sided prediction
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Removing spurious features can hurt accuracy and affect groups disproportionately
Fereshte Khani 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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Counterfactual generative networks
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Robust representation learning via perceptual similarity metrics
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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Diversify and disambiguate: Learning from underspecified data
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