Authors

* External authors

Venue

Date

Share

SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

Yuhta Takida

Takashi Shibuya

Wei-Hsiang Liao

Chieh-Hsin Lai

Junki Ohmura*

Toshimitsu Uesaka

Naoki Murata

Shusuke Takahashi*

Toshiyuki Kumakura*

Yuki Mitsufuji

* External authors

ICML 2022

2022

Abstract

One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also known as codebook collapse. We hypothesize that the training scheme of VQ-VAE, which involves some carefully designed heuristics, underlies this issue. In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE). In SQ-VAE, we observe a trend that the quantization is stochastic at the initial stage of the training but gradually converges toward a deterministic quantization, which we call self-annealing. Our experiments show that SQ-VAE improves codebook utilization without using common heuristics. Furthermore, we empirically show that SQ-VAE is superior to VAE and VQ-VAE in vision- and speech-related tasks.

Related Publications

Music Arena: Live Evaluation for Text-to-Music

NeurIPS, 2025
Yonghyun Kim, Wayne Chi, Anastasios N. Angelopoulos, Wei-Lin Chiang, Koichi Saito, Shinji Watanabe, Yuki Mitsufuji, Chris Donahue

We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare…

Large-Scale Training Data Attribution for Music Generative Models via Unlearning

NeurIPS, 2025
Woosung Choi, Junghyun Koo*, Kin Wai Cheuk, Joan Serrà, Marco A. Martínez-Ramírez, Yukara Ikemiya, Naoki Murata, Yuhta Takida, Wei-Hsiang Liao, Yuki Mitsufuji

This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed to the generation of a particular output from a specific mod…

Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion

NeurIPS, 2025
Michail Dontas, Yutong He, Naoki Murata, Yuki Mitsufuji, J. Zico Kolter*, Ruslan Salakhutdinov*

Blind inverse problems, where both the target data and forward operator are unknown, are crucial to many computer vision applications. Existing methods often depend on restrictive assumptions such as additional training, operator linearity, or narrow image distributions, thu…

  • HOME
  • Publications
  • SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.