LLP-GAN: A GAN-Based Algorithm for Learning From Label Proportions

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 11; pp. 8377 - 8388
• Main Authors: Liu, Jiabin, Wang, Bo, Hang, Hanyuan, Wang, Huadong, Qi, Zhiquan, Tian, Yingjie, Shi, Yong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classifiers; Entropy; Generative adversarial networks; Generative adversarial networks (GANs); Generators; Labels; Learning; learning from label proportions (LLP); Machine learning; Privacy; privacy protection; Representations; Supervised learning; Task analysis; Training; weakly supervised learning
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2022.3149926

Learning from label proportions (LLP) is a widespread and important learning paradigm: only the bag-level proportional information of the grouped training instances is available for the classification task, instead of the instance-level labels in the fully supervised scenario. As a result, LLP is a typical weakly supervised learning protocol and commonly exists in privacy protection circumstances due to the sensitivity in label information for real-world applications. In general, it is less laborious and more efficient to collect label proportions as the bag-level supervised information than the instance-level one. However, the hint for learning the discriminative feature representation is also limited as a less informative signal directly associated with the labels is provided, thus deteriorating the performance of the final instance-level classifier. In this article, delving into the label proportions, we bypass this weak supervision by leveraging generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN. Endowed with an end-to-end structure, LLP-GAN performs approximation in the light of an adversarial learning mechanism without imposing restricted assumptions on distribution. Accordingly, the final instance-level classifier can be directly induced upon the discriminator with minor modification. Under mild assumptions, we give the explicit generative representation and prove the global optimality for LLP-GAN. In addition, compared with existing methods, our work empowers LLP solvers with desirable scalability inheriting from deep models. Extensive experiments on benchmark datasets and a real-world application demonstrate the vivid advantages of the proposed approach.