A Two-Step Disentanglement Method

Abstract

We address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not. Our solution is simpler than previous solutions and employs adversarial training. First, the part of the data that is correlated with the labels is extracted by training a classifier. Then, the other part is extracted such that it enables the reconstruction of the original data but does not contain label information. The utility of the new method is demonstrated on visual datasets as well as on financial data. Our code is available at https://github.com/naamahadad/A-Two-Step-Disentanglement-Method

References in corpus (5)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Semi-Supervised Learning with Deep Generative Models
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Disentangling factors of variation in deep representations using adversarial training