MGGAN: Solving Mode Collapse Using Manifold-Guided Training

Duhyeon Bang, Hyunjung Shim

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

30 Citations (Scopus)

Abstract

Mode collapse is a critical problem in training generative adversarial networks. To alleviate mode collapse, several recent studies have introduced new objective functions, network architectures, or alternative training schemes. However, their achievement is often the result of sacrificing the image quality. In this paper, we propose a new algorithm, namely, the manifold-guided generative adversarial network (MGGAN), which leverages a guidance network on existing GAN architecture to induce the generator to learn the overall modes of a data distribution. The guidance network transforms an image into a learned manifold space, which is effective in representing the coverage of the overall modes. The characteristics of this guidance network helps penalize mode imbalance. Results of the experimental comparisons using various baseline GANs showed that MGGAN can be easily extended to existing GANs and resolve mode collapse without losing the image quality. Moreover, we extend the idea of manifold-guided GAN training to increase the original diversity of a data distribution. From the experiment, we confirmed that a GAN model guided by a joint manifold can sample data distribution with greater diversity. Results of the experimental analysis confirmed that MGGAN is an effective and efficient tool for improving the diversity of GANs.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2347-2356
Number of pages10
ISBN (Electronic)9781665401913
DOIs
Publication statusPublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada
Duration: 2021 Oct 112021 Oct 17

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2021-October
ISSN (Print)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Country/TerritoryCanada
CityVirtual, Online
Period21/10/1121/10/17

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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