BICL uses biased non-uniform transition matrices to generate constrained complementary labels, enabling effective learning and over sevenfold accuracy gains on many-class image datasets.
Solar: Sinkhorn label refinery for imbalanced partial-label learning.Advances in neural information processing systems, 35:8104–8117, 2022
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Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
BICL uses biased non-uniform transition matrices to generate constrained complementary labels, enabling effective learning and over sevenfold accuracy gains on many-class image datasets.