# Disentangling and Learning Robust Representations with Natural Clustering

## TL;DR

This paper introduces N-VAE, a model that disentangles class-specific and shared factors of variation in data, improving generative capabilities and class-dependent factor detection.

## Contribution

The paper proposes N-VAE, a novel model with a class-conditioned and shared latent space for disentangling multimodal generative factors.

## Key findings

- Effective separation of class-specific and shared factors.
- Ability to generate novel samples with unseen characteristics.
- Improved detection of class-dependent generative factors.

## Abstract

Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-VAE, a model which is capable of separating factors of variation which are exclusive to certain classes from factors that are shared among classes. This model implements an explicitly compositional latent variable structure by defining a class-conditioned latent space and a shared latent space. We show its usefulness for detecting and disentangling class-dependent generative factors as well as its capacity to generate artificial samples which contain characteristics unseen in the training data.