Diffusion bridges vector quantized Variational AutoEncoders
- URL: http://arxiv.org/abs/2202.04895v1
- Date: Thu, 10 Feb 2022 08:38:12 GMT
- Title: Diffusion bridges vector quantized Variational AutoEncoders
- Authors: Max Cohen (TSP, IP Paris, SAMOVAR), Guillaume Quispe (CMAP, IP Paris),
Sylvain Le Corff (TSP, IP Paris, SAMOVAR), Charles Ollion (CMAP, IP Paris),
Eric Moulines (CMAP, IP Paris)
- Abstract summary: We show that our model is competitive with the autoregressive prior on the mini-Imagenet dataset.
Our framework also extends the standard VQ-VAE and enables end-to-end training.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Vector Quantised-Variational AutoEncoders (VQ-VAE) are generative models
based on discrete latent representations of the data, where inputs are mapped
to a finite set of learned embeddings.To generate new samples, an
autoregressive prior distribution over the discrete states must be trained
separately. This prior is generally very complex and leads to very slow
generation. In this work, we propose a new model to train the prior and the
encoder/decoder networks simultaneously. We build a diffusion bridge between a
continuous coded vector and a non-informative prior distribution. The latent
discrete states are then given as random functions of these continuous vectors.
We show that our model is competitive with the autoregressive prior on the
mini-Imagenet dataset and is very efficient in both optimization and sampling.
Our framework also extends the standard VQ-VAE and enables end-to-end training.
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