Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Abstract

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training.

References in corpus (8)

• Auto-Encoding Variational Bayes
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• Dual Learning for Machine Translation
• Disentangling factors of variation in deep representations using adversarial training
• Disentangling Factors of Variation via Generative Entangling
• DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
• Challenges in Disentangling Independent Factors of Variation
• Disentangling Factors of Variation by Mixing Them

Cited by in corpus (5)

• Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
• Disentangling Latent Hands for Image Synthesis and Pose Estimation
• A Compression Objective and a Cycle Loss for Neural Image Compression
• Disentanglement for Discriminative Visual Recognition
• A Cyclically-Trained Adversarial Network for Invariant Representation Learning