no code implementations • 2 Nov 2020 • Shang-Fu Chen, Jia-Wei Yan, Ya-Fan Su, Yu-Chiang Frank Wang
Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly.
no code implementations • 21 Oct 2020 • Jia-Wei Yan, Ci-Siang Lin, Fu-En Yang, Yu-Jhe Li, Yu-Chiang Frank Wang
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition.