• DOI:10.1109/ICIP42928.2021.9506015
• Corpus ID: 238689576

Representation Decomposition For Image Manipulation And Beyond

@article{Chen2020RepresentationDF,
  title={Representation Decomposition For Image Manipulation And Beyond},
  author={Shang-Fu Chen and Jia-Wei Yan and Ya Su and Yu-Chiang Frank Wang},
  journal={2021 IEEE International Conference on Image Processing (ICIP)},
  year={2020},
  pages={1169-1173}
}

• Shang-Fu Chen, Jia-Wei Yan, +1 author Yu-Chiang Frank Wang
• Published 2 November 2020
• Computer Science
• 2021 IEEE International Conference on Image Processing (ICIP)

Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly. While existing works like infoGAN [1] and ACGAN [2] 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.