SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping

TL;DR

SW-VAE introduces a weakly-supervised approach for disentangled representation learning by leveraging dataset generative factors and gradually increasing training difficulty, significantly outperforming existing methods.

Contribution

The paper presents SW-VAE, a novel weakly-supervised training method that improves disentanglement by using input pairs and progressive difficulty strategies.

Findings

Significant improvement over SOTA on multiple datasets

Effective use of supervision signals from dataset generative factors

Smooth training process through difficulty escalation strategies

Abstract

Representation disentanglement is an important goal of representation learning that benefits various downstream tasks. To achieve this goal, many unsupervised learning representation disentanglement approaches have been developed. However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning. Therefore, we propose a novel weakly-supervised training approach, named as SW-VAE, which incorporates pairs of input observations as supervision signals by using the generative factors of datasets. Furthermore, we introduce strategies to gradually increase the learning difficulty during training to smooth the training process. As shown on several datasets, our model shows significant improvement over state-of-the-art (SOTA) methods on representation disentanglement tasks.