High-Fidelity Synthesis with Disentangled Representation

13 Jan 2020Wonkwang LeeDonggyun KimSeunghoon HongHonglak Lee

Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disentangled representation learning, existing approaches often suffer from the trade-off between representation learning and generation performance i.e. improving generation quality sacrifices disentanglement performance)... (read more)

PDF Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.