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Wilds: A benchmark of in-the-wild distribution shifts

, , , , , , , , , , and . International Conference on Machine Learning, page 5637--5664. PMLR, (2021)

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Wilds: A benchmark of in-the-wild distribution shifts, , , , , , , , , and 1 other author(s). International Conference on Machine Learning, page 5637--5664. PMLR, (2021)Genotype Specification Language, , , , , , , , and . ACS Synth. Biol., 5 (6): 471--478 (Jun 17, 2016)Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization., , , and . CoRR, (2019)OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models., , , , , , , , , and 6 other author(s). CoRR, (2023)WILDS: A Benchmark of in-the-Wild Distribution Shifts., , , , , , , , , and 6 other author(s). CoRR, (2020)Just Train Twice: Improving Group Robustness without Training Group Information., , , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 6781-6792. PMLR, (2021)Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization., , , , , , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 7721-7735. PMLR, (2021)WILDS: A Benchmark of in-the-Wild Distribution Shifts., , , , , , , , , and 13 other author(s). ICML, volume 139 of Proceedings of Machine Learning Research, page 5637-5664. PMLR, (2021)Distributionally Robust Neural Networks., , , and . ICLR, OpenReview.net, (2020)Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization, , , and . International Conference on Learning Representations (ICLR), (2020)