Name
Title | ||
|---|---|---|
Regularizing Discriminative Capability Of Cgans For Semi-Supervised Generative Learning |
Abstract | ||
|---|---|---|
Semi-supervised generative learning aims to learn the underlying class-conditional distribution of partially labeled data. Generative Adversarial Networks (GANs) have led to promising progress in this task. However, it stil-l needs to further explore the issue of imbalance between real labeled data and fake data in the adversarial learning process. To address this issue, we propose a regularization technique based on Random Regional Replacement (R-3-regularization) to facilitate the generative learning process. Specifically, we construct two types of between-class instances: cross-category ones and real fake ones. These instances could be closer to the decision boundaries and are important for regularizing the classification and discriminative networks in our class-conditional GANs, which we refer to as R-3-CGAN. Better guidance from these two networks makes the generative network produce instances with class-specific information and high fidelity. We experiment with multiple standard benchmarks, and demonstrate that the R-3-regularization can lead to significant improvement in both classification and class-conditional image synthesis. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.00576 | 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
DocType | ISSN | Citations |
Conference | 1063-6919 | 1 |
PageRank | References | Authors |
0.36 | 30 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Liu | 1 | 82 | 29.92 |
| Guangchang Deng | 2 | 1 | 0.36 |
| Xiangping Zeng | 3 | 2 | 2.06 |
| Si Wu | 4 | 148 | 16.73 |
| Zhiwen Yu | 5 | 65 | 10.06 |
| Hau-San Wong | 6 | 1008 | 86.89 |