LAVAE: Disentangling Location and Appearance

Understand

We propose a probabilistic generative model for unsupervised learning of structured, interpretable, object-based representations of visual scenes.

• We use amortized variational inference to train the generative model end-to-end.
• The learned representations of object location and appearance are fully disentangled, and objects are represented independently of each other in the latent space.
• Unlike previous approaches that disentangle location and appearance, ours generalizes seamlessly to scenes with many more objects than encountered in the training regime.