  Disentangled generative models are typically trained with an extra
regularization term, which encourages the traversal of each latent factor to
make a distinct and independent change at the cost of generation quality. When
traversing the latent space of generative models trained without the
disentanglement term, the generated samples show semantically meaningful
change, raising the question: do generative models know disentanglement? We
propose an unsupervised and model-agnostic method: Disentanglement via Contrast
(DisCo) in the Variation Space. DisCo consists of: (i) a Navigator providing
traversal directions in the latent space, and (ii) a $\Delta$-Contrastor
composed of two shared-weight Encoders, which encode image pairs along these
directions to disentangled representations respectively, and a difference
operator to map the encoded representations to the Variation Space. We propose
two more key techniques for DisCo: entropy-based domination loss to make the
encoded representations more disentangled and the strategy of flipping hard
negatives to address directions with the same semantic meaning. By optimizing
the Navigator to discover disentangled directions in the latent space and
Encoders to extract disentangled representations from images with Contrastive
Learning, DisCo achieves the state-of-the-art disentanglement given pretrained
non-disentangled generative models, including GAN, VAE, and Flow. Project page
at https://github.com/xrenaa/DisCo.
