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.

中文翻译：

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通过对抗性学习为多标签分类捕获联合标签分布

标签相关性对于多标签学习很重要。尽管当前的多标签学习方法可以利用一阶、二阶和高阶标签依赖性，但它们无法利用完整的标签相关性，这些相关性包含在真实标签的联合标签分布中。然而，直接对复杂和未知的联合标签分布进行建模是非常具有挑战性的，如果不是不可能的话。在本文中，我们提出了一个对抗性学习框架，以加强地面实况多标签和预测的多个标签的联合分布之间的相似性。具体来说，所提出的多标签学习方法包括多标签分类器和标签鉴别器。分类器最小化预测标签和相应的真实标签之间的误差，并为鉴别器提供容错空间。鉴别器的目的是区分预测标签和真实标签。分类器和鉴别器通过交替过程同时训练。通过对抗性学习，预测的多标签的联合标签分布收敛到真实多标签中固有的联合分布，从而提高了多标签学习的性能，如在 11 个基准数据库上的实验所示。