RAQ-VAE: Rate-Adaptive Vector-Quantized Variational Autoencoder

Jiwan Seo

Keywords: Discrete representation learning, Vector-quantized variational autoencoder, Generative model, Sequence-to-sequence

Abstract: Vector Quantized Variational AutoEncoder (VQ-VAE) is an established technique in machine learning for learning discrete representations across various modalities. However, its scalability and applicability are limited by the need to retrain the model to adjust the codebook for different rate requirements or encoding efficiency. We introduce the Rate-Adaptive VQ-VAE ($\textbf{RAQ-VAE}$) framework, which addresses this challenge with two novel discrete (codebook) representation methods: a model-based approach using a clustering technique for existing pre-trained VQ-VAE models, and a data-driven approach utilizing a sequence-to-sequence (Seq2Seq) model for variable-rate codebook generation. Our experiments demonstrate that RAQ-VAE achieves effective reconstruction performance across multiple rates, often outperforming conventional fixed-rate VQ-VAE models. This work enhances the adaptability and performance of VQ-VAEs, with broad applications in data reconstruction, generation, and computer vision tasks.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Submission Number: 6784