Task-driven dictionary learning based on convolutional neural network features

Tom Tirer, Raja Giryes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Modeling data as a linear combination of a few elements from a learned dictionary has been used extensively in the recent decade in many fields, such as machine learning and signal processing. The learning of the dictionary is usually performed in an unsupervised manner, which is most suitable for regression tasks. However, for other purposes, e.g. image classification, it is advantageous to learn a dictionary from the data in a supervised way. Such an approach has been referred to as task-driven dictionary learning. In this work, we integrate this approach with deep learning. We modify this strategy such that the dictionary is learned for features obtained by a convolutional neural network (CNN). The parameters of the CNN are learned simultaneously with the task-driven dictionary and with the classifier parameters.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1885-1889
Number of pages5
ISBN (Electronic)9789082797015
DOIs
StatePublished - 29 Nov 2018
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Publication series

NameEuropean Signal Processing Conference
Volume2018-September
ISSN (Print)2219-5491

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
Country/TerritoryItaly
CityRome
Period3/09/187/09/18

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