Creation of a Deep Convolutional Auto-Encoder in Caffe

被引：0

作者：

Turchenko, Volodymyr ^{[1
,2
]}

Luczak, Artur ^{[3
]}

机构：

[1] NuraLogix Corp, 200-10 King St E, Toronto, ON M5C 1C3, Canada

[2] Univ Toronto, Ontario Inst Studies Educ, 45 Walmer Rd, Toronto, ON M5R 2X2, Canada

[3] Univ Lethbridge, Canadian Ctr Behav Neurosci, Dept Neurosci, 4401 Univ Dr, Lethbridge, AB T1K 3M4, Canada

来源：

PROCEEDINGS OF THE 2017 9TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS), VOL 2 | 2017年

关键词：

deep convolutional auto-encoder; machine learning; neural networks; visualization; dimensionality reduction;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison with a classic auto-encoder on the example of MNIST dataset.

引用

页码：651 / 659

页数：9

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