Task-Driven Discrete Representation Learning

TL;DR

This paper introduces a task-driven framework for deep discrete representation learning, emphasizing the practical utility of discrete features in downstream tasks and analyzing their theoretical trade-offs.

Contribution

It proposes a unified, task-oriented approach to DRL, moving beyond generative quality to evaluate discrete representations' usefulness for various tasks.

Findings

Discrete representations improve task performance in multiple applications.

Theoretical analysis reveals trade-offs between capacity and sample complexity.

Framework demonstrates flexibility across diverse domains.

Abstract

In recent years, deep discrete representation learning (DRL) has achieved significant success across various domains. Most DRL frameworks (e.g., the widely used VQ-VAE and its variants) have primarily focused on generative settings, where the quality of a representation is implicitly gauged by the fidelity of its generation. In fact, the goodness of a discrete representation remain ambiguously defined across the literature. In this work, we adopt a practical approach that examines DRL from a task-driven perspective. We propose a unified framework that explores the usefulness of discrete features in relation to downstream tasks, with generation naturally viewed as one possible application. In this context, the properties of discrete representations as well as the way they benefit certain tasks are also relatively understudied. We therefore provide an additional theoretical analysis of the trade-off between representational capacity and sample complexity, shedding light on how discrete representation utilization impacts task performance. Finally, we demonstrate the flexibility and effectiveness of our framework across diverse applications.