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.
|Title of host publication||Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published - 2021|
|Event||18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada|
Duration: 2021 Oct 11 → 2021 Oct 17
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Conference||18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021|
|Period||21/10/11 → 21/10/17|
Bibliographical noteFunding Information:
This research was supported by the NRF Korea funded by the MSIT (NRF-2019R1A2C2006123) and the Korea Medical Device Development Fund grant (Project Number: 202011D06).
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition