Learning Discrete Representations via Information Maximizing Self-Augmented Training

TL;DR

This paper introduces IMSAT, a method that leverages data augmentation and information theory to learn discrete data representations, achieving state-of-the-art results in clustering and hash learning.

Contribution

The paper proposes IMSAT, a novel approach combining data augmentation and information maximization for effective discrete representation learning.

Findings

IMSAT outperforms existing methods on benchmark datasets.

It effectively enforces invariance through data augmentation.

Achieves state-of-the-art results in clustering and hash learning.

Abstract

Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in order to learn useful representations that exhibit intended invariance for applications of interest. To this end, we propose a method called Information Maximizing Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose the invariance on discrete representations. More specifically, we encourage the predicted representations of augmented data points to be close to those of the original data points in an end-to-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Extensive experiments on benchmark datasets show that IMSAT produces state-of-the-art results for both clustering and unsupervised hash learning.