RESEARCH

SPEECH & AUDIO

Implicit Rank-Minimizing Autoencoder

October 22, 2020

Abstract

An important component of autoencoders is the method by which the information capacity of the latent representation is minimized or limited. In this work, the rank of the covariance matrix of the codes is implicitly minimized by relying on the fact that gradient descent learning in multi-layer linear networks leads to minimum-rank solutions. By inserting a number of extra linear layers between the encoder and the decoder, the system spontaneously learns representations with a low effective dimension. The model, dubbed Implicit Rank-Minimizing Autoencoder (IRMAE), is simple, deterministic, and learns compact latent spaces. We demonstrate the validity of the method on several image generation and representation learning tasks.

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AUTHORS

Written by

Li Jing

Jure Zbontar

Yann LeCun

Publisher

NeurIPS

Research Topics

Speech & Audio

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