PL EN


Preferences help
enabled [disable] Abstract
Number of results
2015 | 24 |
Article title

Effectiveness of Unsupervised Training in Deep Learning Neural Networks

Content
Title variants
Languages of publication
PL
Abstracts
PL
Deep learning is a field of research attracting nowadays much attention, mainly because deep architectures help in obtaining outstanding results on many vision, speech and natural language processing – related tasks. To make deep learning effective, very often an unsupervised pretraining phase is applied. In this article, we present experimental study evaluating usefulness of such approach, testing on several benchmarks and different percentages of labeled data, how Contrastive Divergence (CD), one of the most popular pretraining methods, influences network generalization.
Publisher
Year
Volume
24
Physical description
Dates
published
2015
online
06 - 07 - 2016
Contributors
References
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.ojs-issn-2083-8476-year-2015-volume-24-article-6333
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.