2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | MLR-APPL-IVASR-3.9 | ||
| Paper Title | REPRESENTATION DECOMPOSITION FOR IMAGE MANIPULATION AND BEYOND | ||
| Authors | Shang-Fu Chen, Jai-Wei Yan, National Taiwan University, Taiwan; Ya-Fan Su, Chunghwa Telecom, Taiwan; Yu-Chiang Frank Wang, National Taiwan University, Taiwan | ||
| Session | MLR-APPL-IVASR-3: Machine learning for image and video analysis, synthesis, and retrieval 3 | ||
| Location | Area E | ||
| Session Time: | Tuesday, 21 September, 08:00 - 09:30 | ||
| Presentation Time: | Tuesday, 21 September, 08:00 - 09:30 | ||
| Presentation | Poster | ||
| Topic | Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | Representation disentanglement aims at learning interpretable features so that the output can be recovered or manipulated accordingly. While existing works like infoGAN and AC-GAN exist, they choose to derive disjoint attribute code for feature disentanglement, which is not applicable for existing/trained generative models. In this paper, we propose a decomposition-GAN (dec-GAN), which is able to achieve the decomposition of an existing latent representation into content and attribute features. Guided by the classifier pre-trained on the attributes of interest, our dec-GAN decomposes the attributes of interest from the latent representation, while data recovery and feature consistency objectives enforce the learning of our proposed method. Our experiments on multiple image datasets confirm the effectiveness and robustness of our dec-GAN over recent representation disentanglement models | ||