DEFT: Distilling Entangled Factors by Preventing Information Diffusion

Abstract

Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning. Many works achieve disentanglement upon information bottlenecks. Despite their elegant mathematical foundations, the IB branch usually exhibits lower performance. In order to provide an insight into the problem, we develop an annealing test to calculate the information freezing point (IFP), which is a transition state to freeze information into the latent variables. We also explore this clue or inductive bias for separating the entangled factors according to the differences in the IFP distributions. We found the existing approaches suffer from the information diffusion problem, according to which the increased information diffuses in all latent variables. Based on this insight, we propose a novel disentanglement framework, termed the distilling entangled factor (DEFT), to address the information diffusion problem by scaling backward information. DEFT applies a multistage training strategy, including multigroup encoders with different learning rates and piecewise pressure, to disentangle the factors stage by stage. We evaluate DEFT on three variants of dSprites and SmallNORB, which shows low-variance and high-level disentanglement scores. Furthermore, the experiment under the correlative factors demonstrates incapable of TC-based approaches. DEFT also exhibits a competitive performance in the unsupervised setting.