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.
Unlike risk-correction methods, CPE focuses on directly estimating the probability of a label being complementary, de- noted as p(¯y|x)
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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.