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Stepwise PathNet: a layer-by-layer knowledge-selection-based transfer learning algorithm.

Abstract：

:Some neural network can be trained by transfer learning, which uses a pre-trained neural network as the source task, for a small target task's dataset. The performance of the transfer learning depends on the knowledge (i.e., layers) selected from the pre-trained network. At present, this knowledge is usually chosen by humans. The transfer learning method PathNet automatically selects pre-trained modules or adjustable modules in a modular neural network. However, PathNet requires modular neural networks as the pre-trained networks, therefore non-modular pre-trained neural networks are currently unavailable. Consequently, PathNet limits the versatility of the network structure. To address this limitation, we propose Stepwise PathNet, which regards the layers of a non-modular pre-trained neural network as the module in PathNet and selects the layers automatically through training. In an experimental validation of transfer learning from InceptionV3 pre-trained on the ImageNet dataset to networks trained on three other datasets (CIFAR-100, SVHN and Food-101), Stepwise PathNet was up to 8% and 10% more accurate than finely tuned and from-scratch approaches, respectively. Also, some of the selected layers were not supported by the layer functions assumed in PathNet.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Imai S,Kawai S,Nobuhara H

doi

10.1038/s41598-020-64165-3

subject

Has Abstract

pub_date

2020-05-18 00:00:00

pages

8132

issue

1

issn

2045-2322

pii

10.1038/s41598-020-64165-3

journal_volume

10

pub_type

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