Gated Variational AutoEncoders: Incorporating Weak Supervision to Encourage Disentanglement

TL;DR

This paper introduces Gated-VAE, a weakly-supervised method that incorporates domain knowledge into VAEs to improve disentanglement of latent factors, demonstrated on face images with better quantitative and qualitative results.

Contribution

It proposes a novel Gated-VAE approach that uses weak supervision and gated backpropagation to enhance disentanglement in VAE models.

Findings

Gated-VAE improves disentanglement, completeness, and informativeness metrics.

Latent factors are better aligned with intended partitions.

Effective even with weak or noisy supervision.

Abstract

Variational AutoEncoders (VAEs) provide a means to generate representational latent embeddings. Previous research has highlighted the benefits of achieving representations that are disentangled, particularly for downstream tasks. However, there is some debate about how to encourage disentanglement with VAEs and evidence indicates that existing implementations of VAEs do not achieve disentanglement consistently. The evaluation of how well a VAE's latent space has been disentangled is often evaluated against our subjective expectations of which attributes should be disentangled for a given problem. Therefore, by definition, we already have domain knowledge of what should be achieved and yet we use unsupervised approaches to achieve it. We propose a weakly-supervised approach that incorporates any available domain knowledge into the training process to form a Gated-VAE. The process involves partitioning the representational embedding and gating backpropagation. All partitions are utilised on the forward pass but gradients are backpropagated through different partitions according to selected image/target pairings. The approach can be used to modify existing VAE models such as beta-VAE, InfoVAE and DIP-VAE-II. Experiments demonstrate that using gated backpropagation, latent factors are represented in their intended partition. The approach is applied to images of faces for the purpose of disentangling head-pose from facial expression. Quantitative metrics show that using Gated-VAE improves average disentanglement, completeness and informativeness, as compared with un-gated implementations. Qualitative assessment of latent traversals demonstrate its disentanglement of head-pose from expression, even when only weak/noisy supervision is available.