Summary of Hq-vae: Hierarchical Discrete Representation Learning with Variational Bayes, by Yuhta Takida et al.
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
by Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chieh-Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, Yuki Mitsufuji
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors propose a novel framework called hierarchically quantized variational autoencoder (HQ-VAE) that stochastically learns hierarchical discrete representations using the variational Bayes framework. HQ-VAE is designed to mitigate the codebook/layer collapse issue in hierarchical extensions of vector quantization (VQ) models, which can lead to degraded reconstruction accuracy. The authors demonstrate the effectiveness of HQ-VAE on image and audio datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HQ-VAE is a new approach that combines the benefits of vector quantization (VQ) and variational autoencoders (VAEs). It allows for efficient representation of data using discrete codebooks, which can improve reconstruction accuracy. The authors test HQ-VAE on image and audio datasets and show that it outperforms traditional VQ models. |
Keywords
* Artificial intelligence * Quantization * Variational autoencoder
