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.
Consistent complementary-label learning via order-preserving losses
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.