Name
Abstract | ||
|---|---|---|
Existing unsupervised disentanglement methods in latent space of the Generative Adversarial Networks (GANs) rely on the analysis and decomposition of pre-trained weight matrix. However, they only consider the weight matrix of the fully connected layers, ignoring the convolutional layers which are indispensable for image processing in modern generative models. This results in the learned latent semantics lack inter-pretability, which is unacceptable for image editing tasks. In this paper, we propose a more generalized closed-form factor-ization of latent semantics in GANs, which takes the convolutionallayers into consideration when searching for the under-lying variation factors. Our method can be applied to a wide range of deep generators with just a few lines of code. Exten-sive experiments on multiple GAN models trained on various datasets show that our approach is capable of not only finding semantically meaningful dimensions, but also maintaining the consistency and interpretability of image content. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICME52920.2022.9859692 | 2022 IEEE International Conference on Multimedia and Expo (ICME) |
Keywords | DocType | ISSN |
Latent Semantic Interpretation,Generative Adversarial Network,Image Synthesis,Deep Learning | Conference | 1945-7871 |
ISBN | Citations | PageRank |
978-1-6654-8564-7 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuehui Wang | 1 | 0 | 0.34 |
| Qing Wang | 2 | 345 | 76.64 |
| Dongyu Zhang | 3 | 151 | 23.10 |