  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.
