Capturing Joint Label Distribution for Multi-Label Classification through Adversarial Learning

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 of the ground truth labels. However, directly modeling the complex and unknown joint label distribution is very challenging, if not impossible. In this paper, we propose an adversarial learning framework to enforce similarity between joint distribution of the ground truth multi-labels and the predicted multiple labels. Specifically, the proposed multi-label learning method includes a multi-label classifier and a label discriminator. The classifier minimizes error between predicted labels and corresponding ground truth labels and gives the discriminator room for error. The object of the discriminator is to distinguish the predicted labels from the ground truth labels. The classifier and discriminator are trained simultaneously through an alternate process. By adversarial learning, the joint label distribution of the predicted multi-labels converges to the joint distribution inherent in the ground truth multi-labels, and thus boosts the performance of multi-label learning as demonstrated in the experiments on 11 benchmark databases.