Face Identity Disentanglement via Latent Space Mapping

Understand

Learning disentangled representations of data is a fundamental problem in artificial intelligence.

• Specifically, disentangled latent representations allow generative models to control and compose the disentangled factors in the synthesis process.
• Current methods, however, require extensive supervision and training, or instead, noticeably compromise quality.
• In this paper, we present a method that learns how to represent data in a disentangled way, with minimal supervision, manifested solely using available pre-trained networks.