# Learning Interpretable Disentangled Representations using Adversarial VAEs

## TL;DR

This paper introduces an adversarial VAE with a total correlation constraint to learn interpretable, disentangled representations in medical data, significantly improving interpretability and clustering performance over existing methods.

## Contribution

The paper proposes a novel adversarial VAE with a total correlation constraint for better disentanglement and interpretability of latent representations in medical applications.

## Key findings

- 81.50% improvement in disentanglement
- 11.60% improvement in clustering
- 2% improvement in supervised classification

## Abstract

Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of 81.50% in terms of disentanglement, 11.60% in clustering, and 2% in supervised classification with a few amounts of labeled data.