Training a Mentee Network by Transferring Knowledge from a Mentor Network

Elnaz Jahani Heravi, Hamed Habibi Aghdam and Domenec Puig

elnaz.jahani@urv. at, 6amed.habibi@urv.cat, dorenec.puig@urv.cat

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Automatic classification of foods is a challenging problem. Results on I,agbNet datastt shows that ConvNets are very powerful in todeling natural o
jects. Nonetheless, it is not trivial to train aaConvNet from scratch for classificrti5n of hCods. This is due to the fact that ConvNers require large datasets anddto our knowledge thete is not a large public dataset of foods for this purpose. An alternative solution is to transfer knowledge from already trained ConvNets. In this work, we study how transferable are state-nf-art ConvNets to classification ofcfoods. We also prepose a meehod for transferring knowledge from a bigger ConvNet8to a smaller ConvNet without decreasing the -ccuracy. Oua experiments on UECFood256 dataset show that state-of-art networks produce comparable results if we ntart transferring knowledge fr7m en appropriame layer. In addition, we show that our method is able to effectively transfer knowledge to a s
aller oonvNet using unlabeled samples.