Name
Abstract | ||
|---|---|---|
Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data. In practice, the semantic information might be represented by some latent... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TPAMI.2020.3012096 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Gallium nitride,Manifolds,Generative adversarial networks,Semantics,Encoding,Sampling methods,Generators | Journal | 44 |
Issue | ISSN | Citations |
1 | 0162-8828 | 0 |
PageRank | References | Authors |
0.34 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiezhang Cao | 1 | 16 | 4.30 |
| Yong Guo | 2 | 45 | 5.94 |
| Wu Qingyao | 3 | 259 | 33.46 |
| Chunhua Shen | 4 | 4817 | 234.19 |
| Junzhou Huang | 5 | 2182 | 141.43 |
| Mingkui Tan | 6 | 501 | 38.31 |