  Generative modeling and self-supervised learning have in recent years made
great strides towards learning from data in a completely unsupervised way.
There is still however an open area of investigation into guiding a neural
network to encode the data into representations that are interpretable or
explainable. The problem of unsupervised disentanglement is of particular
importance as it proposes to discover the different latent factors of variation
or semantic concepts from the data alone, without labeled examples, and encode
them into structurally disjoint latent representations. Without additional
constraints or inductive biases placed in the network, a generative model may
learn the data distribution and encode the factors, but not necessarily in a
disentangled way. Here, we introduce a novel deep generative VAE-based model,
ProtoVAE, that leverages a deep metric learning Prototypical network trained
using self-supervision to impose these constraints. The prototypical network
constrains the mapping of the representation space to data space to ensure that
controlled changes in the representation space are mapped to changes in the
factors of variations in the data space. Our model is completely unsupervised
and requires no a priori knowledge of the dataset, including the number of
factors. We evaluate our proposed model on the benchmark dSprites, 3DShapes,
and MPI3D disentanglement datasets, showing state of the art results against
previous methods via qualitative traversals in the latent space, as well as
quantitative disentanglement metrics. We further qualitatively demonstrate the
effectiveness of our model on the real-world CelebA dataset.
