2024
Transferring disentangled representations: bridging the gap between synthetic and real images
NeurIPS 2024poster
Developing meaningful and efficient representations that separate the fundamental structure of the data generation mechanism is crucial in representation learning. However, Disentangled Representation Learning has not fully shown its potential on real images, because of correlated generative factors…