publication . Preprint . 2014

How transferable are features in deep neural networks?

Yosinski, Jason; Clune, Jeff; Bengio, Yoshua; Lipson, Hod;
Open Access English
  • Published: 06 Nov 2014
Abstract
Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is ...
Subjects
free text keywords: Computer Science - Learning, Computer Science - Neural and Evolutionary Computing
Funded by
NSERC
Project
  • Funder: Natural Sciences and Engineering Research Council of Canada (NSERC)
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