Transfer Learning with Sparse Associative Memories

被引：0

作者：

Jodelet, Quentin ^{[1
,2
]}

Gripon, Vincent ^{[1
]}

Hagiwara, Masafumi ^{[2
]}

机构：

[1] IMT Atlantique, Technopole Brest Iroise, F-29238 Brest, France

[2] Keio Univ, Yagami Campus, Yokohama, Kanagawa 2238522, Japan

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: THEORETICAL NEURAL COMPUTATION, PT I | 2019年 / 11727卷

关键词：

Neural Networks; Associative Memories; Self-organizing Maps; Deep learning; Transfer learning; Incremental learning; Computer vision; NETWORKS;

D O I：

10.1007/978-3-030-30487-4_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design deep neural networks which support incremental learning and that can be (partially) trained in real time on embedded devices. Experiments on the ImageNet dataset and other different domain specific datasets show that it is possible to design more flexible and faster-to-train Neural Networks at the cost of a slight decrease in accuracy.

引用

页码：497 / 512

页数：16

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