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Spurious correlations in training data often lead to robustness issues since models learn to use them as shortcuts.
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Nam, J., Kim, J., Lee, J., Shin, J.: Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation. In: International Conference on Learning Representations (2021)
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Tartaglione, E., Barbano, C.A., Grangetto, M.: End: Entangling and disentangling deep representations for bias correction. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 13508–13517 (2021)
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Idrissi, B.Y., Arjovsky, M., Pezeshki, M., Lopez-Paz, D.: Simple data balancing achieves competitive worst-group-accuracy. In: Conference on Causal Learning and Reasoning. pp. 336–351. PMLR (2022)
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Creager, E., Jacobsen, J.H., Zemel, R.: Environment inference for invariant learning. In: International Conference on Machine Learning. pp. 2189–2200. PMLR (2021)
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. In: 9th International Conference on Learning Representations, ICLR (2021)
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Liu, E.Z., Haghgoo, B., Chen, A.S., Raghunathan, A., Koh, P.W., Sagawa, S., Liang, P., Finn, C.: Just train twice: Improving group robustness without training group information. In: International Conference on Machine Learning. pp. 6781–6792. PMLR (2021)
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2022
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2022
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Singh, M., Gustafson, L., Adcock, A., de Freitas Reis, V., Gedik, B., Kosaraju, R.P., Mahajan, D., Girshick, R., Dollár, P., van der Maaten, L.: Revisiting weakly supervised pre-training of visual perception models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 804–814 (2022)
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