HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
This addresses a specific bottleneck in hierarchical discrete representation learning for researchers in generative modeling, offering an incremental improvement over existing methods like VQ-VAE-2 and RQ-VAE.
The paper tackles the codebook/layer collapse issue in hierarchical vector quantization VAEs, which degrades reconstruction accuracy, and proposes HQ-VAE, a Bayesian framework that enhances codebook usage and improves reconstruction performance on image and audio datasets.
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model, VQ-VAE, which can be further extended to hierarchical structures for making high-fidelity reconstructions. However, such hierarchical extensions of VQ-VAE often suffer from the codebook/layer collapse issue, where the codebook is not efficiently used to express the data, and hence degrades reconstruction accuracy. To mitigate this problem, we propose a novel unified framework to stochastically learn hierarchical discrete representation on the basis of the variational Bayes framework, called hierarchically quantized variational autoencoder (HQ-VAE). HQ-VAE naturally generalizes the hierarchical variants of VQ-VAE, such as VQ-VAE-2 and residual-quantized VAE (RQ-VAE), and provides them with a Bayesian training scheme. Our comprehensive experiments on image datasets show that HQ-VAE enhances codebook usage and improves reconstruction performance. We also validated HQ-VAE in terms of its applicability to a different modality with an audio dataset.