Name
Abstract | ||
|---|---|---|
A style-based generative adversarial network (StyleGAN2) yields remarkable results in image-to-latent embedding. This work proposes a Detached Dual-channel Domain Encoder as an effective and robust method to embed an image to a latent code, i.e., GAN inversion. It infers a latent code from two aspects: a) a detached dual-channel design to support faithful image reconstruction; and b) a local skip connection that allows conveying pieces of information with image details. We further introduce a hierarchical progressive training strategy that allows the proposed encoder to separately capture different semantic features. The qualitative and quantitative experimental results show that the well-trained encoder can embed an image into a latent code in StyleGAN2 latent space with less time than its peers while preserving facial identity and image details well. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LSP.2021.3059371 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Semantics, Training, Image reconstruction, Gallium nitride, Optimization, Image quality, Generative adversarial networks, Deep learning, generative adversarial networks, image reconstruction, latent code optimization | Journal | 28 |
ISSN | Citations | PageRank |
1070-9908 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nan Yang | 1 | 0 | 1.01 |
| MengChu Zhou | 2 | 8989 | 534.94 |
| Bingjie Xia | 3 | 0 | 0.68 |
| Xiwang Guo | 4 | 65 | 6.29 |
| Liang Qi | 5 | 156 | 27.14 |