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cs.LG2023
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
Wei Wang, Takashi Ishida, Yu-Jie Zhang +2
Complementary-label learning is a weakly supervised learning problem in which each training example is associated with one or multiple complementary labels indicating the classes t…
cs.LG2023★ 2 cited
Binary Classification with Confidence Difference
Wei Wang, Lei Feng, Yuchen Jiang +3
Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. Howev…