  Disentangled generative models map a latent code vector to a target space,
while enforcing that a subset of the learned latent codes are interpretable and
associated with distinct properties of the target distribution. Recent advances
have been dominated by Variational AutoEncoder (VAE)-based methods, while
training disentangled generative adversarial networks (GANs) remains
challenging. In this work, we show that the dominant challenges facing
disentangled GANs can be mitigated through the use of self-supervision. We make
two main contributions: first, we design a novel approach for training
disentangled GANs with self-supervision. We propose contrastive regularizer,
which is inspired by a natural notion of disentanglement: latent traversal.
This achieves higher disentanglement scores than state-of-the-art VAE- and
GAN-based approaches. Second, we propose an unsupervised model selection scheme
called ModelCentrality, which uses generated synthetic samples to compute the
medoid (multi-dimensional generalization of median) of a collection of models.
The current common practice of hyper-parameter tuning requires using
ground-truths samples, each labelled with known perfect disentangled latent
codes. As real datasets are not equipped with such labels, we propose an
unsupervised model selection scheme and show that it finds a model close to the
best one, for both VAEs and GANs. Combining contrastive regularization with
ModelCentrality, we improve upon the state-of-the-art disentanglement scores
significantly, without accessing the supervised data.
