Balance of Number of Embedding and their Dimensions in Vector Quantization

TL;DR

This paper investigates the balance between embedding dimensions and codebook size in vector quantization, proposing an adaptive method that dynamically optimizes these parameters to improve VQ-VAE performance.

Contribution

It introduces a novel adaptive dynamic quantization approach using Gumbel-Softmax to automatically select optimal codebook configurations during training.

Findings

Augmenting codebook size while reducing embedding dimensions enhances VQ-VAE effectiveness.

The proposed method outperforms static hyperparameter settings across benchmark datasets.

Dynamic quantization significantly improves model flexibility and performance.

Abstract

The dimensionality of the embedding and the number of available embeddings ( also called codebook size) are critical factors influencing the performance of Vector Quantization(VQ), a discretization process used in many models such as the Vector Quantized Variational Autoencoder (VQ-VAE) architecture. This study examines the balance between the codebook sizes and dimensions of embeddings in VQ, while maintaining their product constant. Traditionally, these hyper parameters are static during training; however, our findings indicate that augmenting the codebook size while simultaneously reducing the embedding dimension can significantly boost the effectiveness of the VQ-VAE. As a result, the strategic selection of codebook size and embedding dimensions, while preserving the capacity of the discrete codebook space, is critically important. To address this, we propose a novel adaptive dynamic quantization approach, underpinned by the Gumbel-Softmax mechanism, which allows the model to autonomously determine the optimal codebook configuration for each data instance. This dynamic discretizer gives the VQ-VAE remarkable flexibility. Thorough empirical evaluations across multiple benchmark datasets validate the notable performance enhancements achieved by our approach, highlighting the significant potential of adaptive dynamic quantization to improve model performance.