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.
|Title of host publication||VISIGRAPP 2019 -Proceedings of the 14th International Joint Conference on Computer Vision|
|Editors||Alain Tremeau, Giovanni Maria Farinella, Jose Braz|
|Publication status||Published - 2019|
|Event||VISIGRAPP 2019: 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Prague, Czech Republic|
Duration: 25 Feb 2019 → 27 Feb 2019
|Period||25/02/19 → 27/02/19|
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 (Eds.), VISIGRAPP 2019 -Proceedings of the 14th International Joint Conference on Computer Vision (Vol. 5, pp. 143-154). SciTePress. https://doi.org/10.5220/0007688801430154