This month of its monthly seminar SagivTech hosted Elad Hoffer from the Technion.
Elad holds a BSc and MSc both from the Electrical Engineering department at the Technion. Elad is a PhD candidate at the Technion under the supervision of Prof. Nir Ailon. His research is focused on Deep Learning of representations, and related topics of machine learning and computer vision.
Triplet networks let us learn a distance metric between images, and have been shown to give better results than other methods like Siamese networks. This kind of metric learning has been since used in many applications like face recognition and image search.
When adding spatial contrasting, it is possible to train deep networks for metric learning on unlabelled data or partially labelled data and obtain representations useful for other tasks, giving state of the art results.
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