Abstract

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.

Original languageEnglish (US)
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5385-5394
Number of pages10
Volume2017-January
ISBN (Electronic)9781538604571
DOIs
StatePublished - Nov 6 2017
Event30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States
Duration: Jul 21 2017Jul 26 2017

Other

Other30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017
CountryUnited States
CityHonolulu
Period7/21/177/26/17

Fingerprint

Learning algorithms
Computer vision
Learning systems
Personnel
Deep neural networks
Deep learning

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

Cite this

Demakethepalli Venkateswara, H., Eusebio, J., Chakraborty, S., & Panchanathan, S. (2017). Deep hashing network for unsupervised domain adaptation. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (Vol. 2017-January, pp. 5385-5394). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVPR.2017.572

Deep hashing network for unsupervised domain adaptation. / Demakethepalli Venkateswara, Hemanth; Eusebio, Jose; Chakraborty, Shayok; Panchanathan, Sethuraman.

Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 5385-5394.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Demakethepalli Venkateswara, H, Eusebio, J, Chakraborty, S & Panchanathan, S 2017, Deep hashing network for unsupervised domain adaptation. in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 5385-5394, 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, United States, 7/21/17. https://doi.org/10.1109/CVPR.2017.572
Demakethepalli Venkateswara H, Eusebio J, Chakraborty S, Panchanathan S. Deep hashing network for unsupervised domain adaptation. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 5385-5394 https://doi.org/10.1109/CVPR.2017.572
Demakethepalli Venkateswara, Hemanth ; Eusebio, Jose ; Chakraborty, Shayok ; Panchanathan, Sethuraman. / Deep hashing network for unsupervised domain adaptation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 5385-5394
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