A Modified Self-training Method for Adapting Domains in the Task of Food Classification

Elnaz Jahani Heravi, Hamed Habibi Aghdam, Domenec Puig

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.
Originalspracheenglisch
TitelVISIGRAPP 2019 -Proceedings of the 14th International Joint Conference on Computer Vision
Redakteure/-innenAlain Tremeau, Giovanni Maria Farinella, Jose Braz
Herausgeber (Verlag)SciTePress
Seiten143-154
Band5
ISBN (elektronisch) 978-989758354-4
DOIs
PublikationsstatusVeröffentlicht - 2019
Extern publiziertJa
VeranstaltungVISIGRAPP 2019: 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Prague, Tschechische Republik
Dauer: 25 Feb 201927 Feb 2019

Konferenz

KonferenzVISIGRAPP 2019
LandTschechische Republik
OrtPrague
Zeitraum25/02/1927/02/19

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    Jahani Heravi, E., Habibi Aghdam, H., & Puig, D. (2019). A Modified Self-training Method for Adapting Domains in the Task of Food Classification. in A. Tremeau, G. M. Farinella, & J. Braz (Hrsg.), VISIGRAPP 2019 -Proceedings of the 14th International Joint Conference on Computer Vision (Band 5, S. 143-154). SciTePress. https://doi.org/10.5220/0007688801430154