Learning Latent Subspaces in Variational Autoencoders

TL;DR

This paper introduces the Conditional Subspace VAE (CSVAE), a novel model that learns interpretable and manipulable latent subspaces correlated with specific labels, enhancing attribute control in generative models.

Contribution

The paper proposes CSVAE, a VAE variant that learns low-dimensional, interpretable latent subspaces linked to labels using mutual information minimization.

Findings

Successfully extracts label-related features in latent subspaces

Enables attribute manipulation on face datasets

Improves interpretability of learned representations

Abstract

Variational autoencoders (VAEs) are widely used deep generative models capable of learning unsupervised latent representations of data. Such representations are often difficult to interpret or control. We consider the problem of unsupervised learning of features correlated to specific labels in a dataset. We propose a VAE-based generative model which we show is capable of extracting features correlated to binary labels in the data and structuring it in a latent subspace which is easy to interpret. Our model, the Conditional Subspace VAE (CSVAE), uses mutual information minimization to learn a low-dimensional latent subspace associated with each label that can easily be inspected and independently manipulated. We demonstrate the utility of the learned representations for attribute manipulation tasks on both the Toronto Face and CelebA datasets.