Capturing Joint Label Distribution for Multi-Label Classification Through Adversarial Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2019.2922603
Abstract: Label correlations are important for multi-label learning. Although current multi-label learning approaches can exploit first-order, second-order, and high-order label dependencies, they fail to exploit complete label correlations, which are included in the joint label distribution… read more here.
Keywords: label; label distribution; multi label; joint label ... See more keywords