Group-disentangled Representation Learning with Weakly-Supervised Regularization

TL;DR

This paper introduces GroupVAE, a novel regularization method for learning group-disentangled representations with weak supervision, improving interpretability and downstream task performance.

Contribution

The paper proposes GroupVAE, a KL divergence-based regularization technique that enhances group-disentanglement and generalization in representation learning.

Findings

GroupVAE significantly improves group disentanglement.

Learning group-disentangled representations benefits downstream tasks.

GroupVAE is competitive with supervised methods.

Abstract

Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning. We investigate learning group-disentangled representations for groups of factors with weak supervision. Existing techniques to address this challenge merely constrain the approximate posterior by averaging over observations of a shared group. As a result, observations with a common set of variations are encoded to distinct latent representations, reducing their capacity to disentangle and generalize to downstream tasks. In contrast to previous works, we propose GroupVAE, a simple yet effective Kullback-Leibler (KL) divergence-based regularization across shared latent representations to enforce consistent and disentangled representations. We conduct a thorough evaluation and demonstrate that our GroupVAE significantly improves group disentanglement. Further, we demonstrate that learning group-disentangled representations improve upon downstream tasks, including fair classification and 3D shape-related tasks such as reconstruction, classification, and transfer learning, and is competitive to supervised methods.