Learning Latent Subspaces in Variational Autoencoders
This work addresses the challenge of interpretability and control in unsupervised learning for researchers and practitioners in generative modeling, though it is incremental as it builds on existing VAE frameworks.
The paper tackles the problem of learning interpretable and controllable latent representations in variational autoencoders by proposing a Conditional Subspace VAE (CSVAE) that extracts features correlated to binary labels and structures them in an easily manipulated latent subspace, demonstrating its utility on Toronto Face and CelebA datasets.
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.