Group-disentangled Representation Learning with Weakly-Supervised Regularization

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.

References in corpus (11)

• Auto-Encoding Variational Bayes
• Categorical Reparameterization with Gumbel-Softmax
• The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
• Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
• Disentangling by Factorising
• Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
• Flexibly Fair Representation Learning by Disentanglement
• From Variational to Deterministic Autoencoders
• Weakly-Supervised Disentanglement Without Compromises
• Learning Latent Subspaces in Variational Autoencoders
• Consistency Regularization for Variational Auto-Encoders