Variational Information Bottleneck on Vector Quantized Autoencoders

Autor:	Wu, Hanwei, Flierl, Markus
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this paper, we provide an information-theoretic interpretation of the Vector Quantized-Variational Autoencoder (VQ-VAE). We show that the loss function of the original VQ-VAE can be derived from the variational deterministic information bottleneck (VDIB) principle. On the other hand, the VQ-VAE trained by the Expectation Maximization (EM) algorithm can be viewed as an approximation to the variational information bottleneck(VIB) principle.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1808.01048 Zobrazit plný text záznamu View this record from Arxiv