Combating Mode Collapse via Offline Manifold Entropy Estimation

Haozhe Liu, Bing Li, Haoqian Wu, Hanbang Liang, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications in recent years. However, mode collapse remains a critical problem in GANs. In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs. Different from existing methods, we propose to generalize the discriminator as feature embedding and maximize the entropy of distributions in the embedding space learned by the discriminator. Specifically, two regularization terms, i.e., Deep Local Linear Embedding (DLLE) and Deep Isometric feature Mapping (DIsoMap), are introduced to encourage the discriminator to learn the structural information embedded in the data, such that the embedding space learned by the discriminator can be well-formed. Based on the well-learned embedding space supported by the discriminator, a non-parametric entropy estimator is designed to efficiently maximize the entropy of embedding vectors, playing as an approximation of maximizing the entropy of the generated distribution. By improving the discriminator and maximizing the distance of the most similar samples in the embedding space, our pipeline effectively reduces the mode collapse without sacrificing the quality of generated samples. Extensive experimental results show the effectiveness of our method which outperforms the GAN baseline, MaF-GAN on CelebA (9.13 vs. 12.43 in FID) and surpasses the recent state-of-the-art energy-based model on the ANIMEFACE dataset (2.80 vs. 2.26 in Inception score).
Original languageEnglish (US)
Title of host publicationProceedings of the AAAI Conference on Artificial Intelligence
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages8834-8842
Number of pages9
DOIs
StatePublished - Jun 26 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-07-04
Acknowledgements: This work was supported by the King Abdullah University of Science and Technology (KAUST) Offce of Sponsored Research through the Visual Computing Center (VCC) funding, the Key-Area Research and Development Program of Guangdong Province, China (No. 2018B010111001), National Key R&D Program of China (2018YFC2000702) and the Scientifc and Technical Innovation 2030-”New Generation Artifcial Intelligence” Project (No. 2020AAA0104100).

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