VQ-DRAW: A Sequential Discrete VAE

By Alex Nichol. 2020

3 citations recorded by the source corpus.

In this paper, I present VQ-DRAW, an algorithm for learning compact discrete representations of data. VQ-DRAW leverages a vector quantization effect to adapt the sequential generation scheme of DRAW to discrete latent variables. I show that VQ-DRAW can effectively learn to compress images from a variety of common datasets, as well as generate realistic samples from these datasets with no help from an autoregressive prior.

Subfields: Artificial Intelligence, Generative Models, Signal Processing

Citations in the corpus

• Denoising Diffusion Probabilistic Models (2020, neutral): “Our progressive decoding argument can be seen in convolutional DRAW and related models [16, 37] and may also lead to more general designs for subscale orderings or future predictions for autoregressive models [35, 61].”