Learning from label proportions with generative adversarial networks

Jiabin Liu, Bo Wang, Zhiquan Qi*, Yingjie Tian, Yong Shi

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

23 Citations (Scopus)

Abstract

In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available. Endowed with end-to-end structure, LLP-GAN performs approximation in the light of an adversarial learning mechanism, without imposing restricted assumptions on distribution. Accordingly, we can directly induce the final instance-level classifier upon the discriminator. Under mild assumptions, we give the explicit generative representation and prove the global optimality for LLP-GAN. Additionally, compared with existing methods, our work empowers LLP solver with capable scalability inheriting from deep models. Several experiments on benchmark datasets demonstrate vivid advantages of the proposed approach.

Original languageEnglish
JournalAdvances in Neural Information Processing Systems
Volume32
Publication statusPublished - 2019
Externally publishedYes
Event33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019 - Vancouver, Canada
Duration: 8 Dec 201914 Dec 2019

Fingerprint

Dive into the research topics of 'Learning from label proportions with generative adversarial networks'. Together they form a unique fingerprint.

Cite this