Support matching: A novel regularization to escape from mode collapse in GANs
- Publisher:
- Springer International Publishing
- Publication Type:
- Conference Proceeding
- Citation:
- Communications in Computer and Information Science, 2019, 1142 CCIS, pp. 40-48
- Issue Date:
- 2019-01-01
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
Support Matching.pdf | Published version | 1.68 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Generative adversarial network (GAN) is an implicit generative model known for its ability to generate sharp images. However, it is poor at generating diverse data, which refers to the mode collapse problem. It turns out that GAN is prone to emphasizing the quality of samples but ignoring their diversity. When mode collapse happens, the support of the generated data distribution is not aligned with that of the real data distribution. We thus propose Support Regularized-GAN (SR-GAN) to address such a mode collapse issue by matching their support. Our experiments on synthetic and real-world datasets show that our regularization can mitigate the mode collapse and also improve the data quality.
Please use this identifier to cite or link to this item: