# Neural Discrete Representation Learning

**Authors:** Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu  
**arXiv:** [1711.00937](https://arxiv.org/abs/1711.00937)  
**Date:** 2017-11-02  
**Summary page:** https://www.summarizepaper.com/en/paper/1711.00937v1/

## Key points

- Machine learning faces challenges in learning useful representations without supervision.
- Authors Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu introduce the Vector Quantised-Variational AutoEncoder (VQ-VAE) to address this challenge.
- VQ-VAE differs from traditional VAEs by producing discrete codes instead of continuous ones and having a dynamically learned prior.
- Incorporation of vector quantisation (VQ) helps the VQ-VAE overcome issues like "posterior collapse" observed in VAE frameworks.
- The model excels in generating high-quality images, videos, and speech, as well as tasks like speaker conversion and unsupervised learning of phonemes.
- The innovative approach and model design provide compelling evidence for the effectiveness of discrete representation learning in various machine learning applications.

## Summary

In the field of machine learning, one of the key challenges is learning useful representations without supervision. In their paper titled "Neural Discrete Representation Learning," authors Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu introduce a novel generative model known as the Vector Quantised-Variational AutoEncoder (VQ-VAE) to tackle this issue. The VQ-VAE model stands out from traditional Variational AutoEncoders (VAEs) in two significant ways: first, its encoder network produces discrete codes instead of continuous ones; and second, the prior in the model is learned dynamically rather than being static. To achieve a discrete latent representation, the authors incorporate concepts from vector quantisation (VQ) into their model. By utilizing the VQ method, the VQ-VAE effectively overcomes issues such as "posterior collapse," where latent variables are disregarded when paired with a powerful autoregressive decoder - a common problem observed in VAE frameworks. Furthermore, by combining these discrete representations with an autoregressive prior, the model demonstrates impressive capabilities in generating high-quality images, videos, and speech. Additionally, it excels in tasks like speaker conversion and unsupervised learning of phonemes - showcasing the practical utility of learned representations. Overall, through their innovative approach and model design  provide compelling evidence for the effectiveness of discrete representation learning in various applications within machine learning.