loading
Papers

Research.Publish.Connect.

Paper

Authors: Elnaz Heravi 1 ; Hamed Aghdam 2 and Domenec Puig 1

Affiliations: 1 Department of Computer Engineering and Mathematics, University Rovira i Virgili, Spain ; 2 The Computer Vision Center, University Autonoma Barcelona, Spain

ISBN: 978-989-758-354-4

Keyword(s): Domain Adaptation, Deep Learning, Food Recognition.

Abstract: Food trackers are tools that recognize foods using their images. In the core of these tools there is usually a neural network that performs the classification. Neural networks are highly expressive models that need a large dataset to generalize well. Since it is hard to collect a training set that captures most of realistic situations in real world, there is usually a shift between the training set and the actual test set. This potentially reduces the performance of the network. In this paper, we propose a method based on self-training to perform unsupervised domain adaptation in the task of food classification. Our method takes into account the uncertainty of predictions instead of probability scores to assign pseudo-labels. Our experiments on the Food-101 and the UPMC-101 datasets show that the proposed method produces more accurate results compared to Tri-training method which had previously surpassed other domain adaptation methods.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.191.168

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Heravi, E.; Aghdam, H. and Puig, D. (2019). A Modified Self-training Method for Adapting Domains in the Task of Food Classification.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 143-154. DOI: 10.5220/0007688801430154

@conference{visapp19,
author={Elnaz Jahani Heravi. and Hamed H. Aghdam. and Domenec Puig.},
title={A Modified Self-training Method for Adapting Domains in the Task of Food Classification},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={143-154},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007688801430154},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - A Modified Self-training Method for Adapting Domains in the Task of Food Classification
SN - 978-989-758-354-4
AU - Heravi, E.
AU - Aghdam, H.
AU - Puig, D.
PY - 2019
SP - 143
EP - 154
DO - 10.5220/0007688801430154

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.