2021
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA
NeurIPS 2021poster
We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to extend the identifiability theory of deep generative models for a very broad class of structured models. While previous…