Publication detail

Deep Learning on Small Datasets using Online Image Search

KOLÁŘ, M. HRADIŠ, M. ZEMČÍK, P.

Original Title

Deep Learning on Small Datasets using Online Image Search

Type

conference paper

Language

English

Original Abstract

Our contribution has the ability to learn visual categories from fewer images than previous approaches. We do this by modifying the pseudolabel method which augments labelled training images with unlabelled images, to create a method capable of handling labelled training images as well as queried images, which are likely to belong to the desired class. This is achieved by modifying the weighting and selection processes. The presented method adapts the pseudolabel approach to allow the use of web-scale datasets of millions of images. The results are demonstrated on a toy problem&start=0&order=1 devised from the SUN 397 dataset, and on the full SUN 397 dataset expanded with images gathered from Google's image search without human intervention.

Keywords

convolutional neural network, deep learning, image classification, reinforcement learning

Authors

KOLÁŘ, M.; HRADIŠ, M.; ZEMČÍK, P.

Released

4. 4. 2016

Publisher

Comenius University in Bratislava

Location

Bratislava

ISBN

978-1-4503-3693-2

Book

Proceedings of 32nd Spring Conference on Computer Graphics

ISBN

1335-5694

Periodical

Proceeding of Spring Conference on Computer Graphics

Year of study

2016

Number

32

State

Slovak Republic

Pages from

87

Pages to

93

Pages count

7

URL

http://dl.acm.org/citation.cfm?id=2948633

BibTex

@inproceedings{BUT130963,
  author="Martin {Kolář} and Michal {Hradiš} and Pavel {Zemčík}",
  title="Deep Learning on Small Datasets using Online Image Search",
  booktitle="Proceedings of 32nd Spring Conference on Computer Graphics",
  year="2016",
  journal="Proceeding of Spring Conference on Computer Graphics",
  volume="2016",
  number="32",
  pages="87--93",
  publisher="Comenius University in Bratislava",
  address="Bratislava",
  doi="10.1145/2948628.2948633",
  isbn="978-1-4503-3693-2",
  issn="1335-5694",
  url="http://dl.acm.org/citation.cfm?id=2948633"
}