NEURAL3D

Disentanglement is a widely used concept and one of the most ambitious challenges in learning. Yet, it still lacks a widely accepted formal definition. The challenge is to develop learning algorithms that disentangle the different factors of variation in the data, but what exactly that means highly depends on the application at hand. For instance, in order to be able to modify content of an indoor scene, we seek algorithms to learn situated and semantic encodings (e.g., positions and colors of objects or lighting). Several mathematical ideas have been proposed to provide a formal definition of disentanglement (e.g., statistical independence, flattening manifolds, irreducible representations of groups, direct products of group actions, but none of them applies to all practical settings where disentanglement is used. We aim to develop a more generally applicable formal definition and to study theoretical guarantees of what can be achieved with disentanglement... Autoencoders are at the..