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Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
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Variance reduction in sgd by distributed importance sampling
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Deep residual learning for image recognition
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
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A simple framework for contrastive learning of visual representations
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Shortcut learning in deep neural networks
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Momentum contrast for unsupervised visual representation learning
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Large-scale methods for distributionally robust optimization
Levy, D., Carmon, Y., Duchi, J. C., and Sidford, A · 2020
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Object-centric learning with slot attention
Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., and Kipf, T · 2020
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The pitfalls of simplicity bias in neural networks
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Simple data balancing achieves competitive worst-group-accuracy
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
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Neural systematic binder
Singh, G., Kim, Y., and Ahn, S · 2022
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Self-supervised visual representation learning with semantic grouping
Wen, X., Zhao, B., Zheng, A., Zhang, X., and Qi, X · 2022
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ZIN: When and how to learn invariance without environment partition?
Yong, L., Zhu, S., Tan, L., and Cui, P · 2022
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Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations
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No subclass left behind: Fine-grained robustness in coarse-grained classification problems
Sohoni, N., Dunnmon, J., Angus, G., Gu, A., and Ré, C · 2020
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Emerging properties in self-supervised vision transformers
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Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering
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Genesis-v2: Inferring unordered object representations without iterative refinement
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On feature decorrelation in self-supervised learning
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Biaswap: Removing dataset bias with bias-tailored swapping augmentation
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Zhang, M., Sohoni, N. S., Zhang, H. R., Finn, C., and Ré, C · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
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Diversify and disambiguate: Out-of-distribution robustness via disagreement
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A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others
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Simple and fast group robustness by automatic feature reweighting
Qiu, S., Potapczynski, A., Izmailov, P., and Wilson, A. G · 2023
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Bridging the gap to real-world object-centric learning
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Group robust classification without any group information
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Discover and cure: Concept-aware mitigation of spurious correlation
Wu, S., Yuksekgonul, M., Zhang, L., and Zou, J · 2023
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Unlocking slot attention by changing optimal transport costs
Zhang, Y., Zhang, D. W., Lacoste-Julien, S., Burghouts, G. J., and Snoek, C. G. M · 2023
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Identifying spurious biases early in training through the lens of simplicity bias
Yang, Y., Gan, E., Dziugaite, G. K., and Mirzasoleiman, B · 2024
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