Semi-Supervised Disentanglement of Class-Related and Class-Independent Factors in VAE

TL;DR

This paper introduces a semi-supervised variational autoencoder framework that disentangles class-related and class-independent factors using attention mechanisms, mixture models, and a novel objective to improve interpretability and representation quality.

Contribution

It proposes a novel semi-supervised VAE framework with attention and mixture priors for disentangling class-related factors, enhancing interpretability and handling multimodal data.

Findings

Successfully disentangles class-related and class-independent factors.

Achieves interpretable features through semi-supervised training.

Demonstrates superior performance on various datasets.

Abstract

In recent years, extending variational autoencoder's framework to learn disentangled representations has received much attention. We address this problem by proposing a framework capable of disentangling class-related and class-independent factors of variation in data. Our framework employs an attention mechanism in its latent space in order to improve the process of extracting class-related factors from data. We also deal with the multimodality of data distribution by utilizing mixture models as learnable prior distributions, as well as incorporating the Bhattacharyya coefficient in the objective function to prevent highly overlapping mixtures. Our model's encoder is further trained in a semi-supervised manner, with a small fraction of labeled data, to improve representations' interpretability. Experiments show that our framework disentangles class-related and class-independent factors of variation and learns interpretable features. Moreover, we demonstrate our model's performance with quantitative and qualitative results on various datasets.