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Deep classifiers are known to be sensitive to data distribution shifts, primarily due to their reliance on spurious correlations in training data.
Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 1902
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Intrinsic dimension of data representations in deep neural networks
Alessio Ansuini, Alessandro Laio, Jakob H. Macke, and Davide Zoccolan · 1905
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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 · 1905
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Dimensionality compression and expansion in Deep Neural Networks
Stefano Recanatesi, Matthew Farrell, Madhu Advani, Timothy Moore, Guillaume Lajoie, and Eric Shea-Brown · 1906
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 1907
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 1907
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Enhancing Adversarial Example Transferability with an Intermediate Level Attack
Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, and Ser-Nam Lim · 1907
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Learning De-biased Representations with Biased Representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, and Seong Joon Oh · 1910
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Decoupling Representation and Classifier for Long-Tailed Recognition
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Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang · 1911
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Gradient-based learning applied to document recognition
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Learning Multiple Layers of Features from Tiny Images
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
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Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2007
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Deep Residual Learning for Image Recognition
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ImageNet Large Scale Visual Recognition Challenge
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TorchVision: PyTorch’s computer vision library
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Densely connected convolutional networks
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Adam: A Method for Stochastic Optimization
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Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
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On feature learning in the presence of spurious correlations
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Principled and efficient transfer learning of deep models via neural collapse
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Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations
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Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations
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Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
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A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others
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The tunnel effect: Building data representations in deep neural networks
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Feature learning in deep classifiers through Intermediate Neural Collapse
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A separability-based approach to quantifying generalization: which layer is best?
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Zero-shot generalization across architectures for visual classification
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Do Deep Neural Network Solutions Form a Star Domain?
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