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Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence.

, , and . NeurIPS, page 11809-11820. (2021)

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On the Pitfall of Mixup for Uncertainty Calibration., , , , and . CVPR, page 7609-7618. IEEE, (2023)Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning., , and . AAAI, page 15888-15896. AAAI Press, (2024)Learning from Noisy Labels with Complementary Loss Functions., , , and . AAAI, page 10111-10119. AAAI Press, (2021)Revisiting Consistency Regularization for Deep Partial Label Learning., , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 24212-24225. PMLR, (2022)Partial-Label Regression., , , , and . AAAI, page 7140-7147. AAAI Press, (2023)Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence., , and . NeurIPS, page 11809-11820. (2021)Partial Label Learning with Gradually Induced Error-Correction Output Codes., , and . ICONIP (1), volume 13623 of Lecture Notes in Computer Science, page 200-211. Springer, (2022)Learning from Complementary Labels via Partial-Output Consistency Regularization., , and . IJCAI, page 3075-3081. ijcai.org, (2021)Learning From Noisy Labels via Dynamic Loss Thresholding., , , , , and . IEEE Trans. Knowl. Data Eng., 36 (11): 6503-6516 (November 2024)Calibration Bottleneck: Over-compressed Representations are Less Calibratable., and . ICML, OpenReview.net, (2024)