Efficient Learning of Sparse Representations with an Energy-Based Model

Abstract

We describe a novel unsupervised method for learning sparse, overcomplete features. The model uses a linear encoder, and a linear decoder preceded by a sparsifying non-linearity that turns a code vector into a quasi-binary sparse code vector. Given an input, the optimal code minimizes the distance between the output of the decoder and the input patch while… (More)
View Slides

Topics

Figures and Tables

Sorry, we couldn't extract any figures or tables for this paper.

Statistics

050100'06'07'08'09'10'11'12'13'14'15'16'17'18
Citations per Year

889 Citations

Semantic Scholar estimates that this publication has 889 citations based on the available data.

See our FAQ for additional information.

Slides referencing similar topics