2025
ComRank: Ranking Loss for Multi-Label Complementary Label Learning
This work proposes ComRank, a ranking loss framework for MLCLL, which encourages complementary labels to be ranked lower than non-complementary ones, thereby modeling pairwise label relationships and ensures Bayes consistency under both uniform and biased cases.
Jin Zhu, Yi Gao, Miao Xu et al.
· Neural Information Processin... · 0 citations