Rank-based unsupervised learning through deep learning in diverse domains

Abstract

Ranking-based unsupervised learning methods have been established as a solution to increase the effectiveness of content-based searches without requiring user intervention. These methods exploit contextual relationships among images, usually encoded in the distance/similarity information of the collections. Concomitantly, strategies based on deep learning have assumed a prominent position in the most diverse machine learning tasks. Thus, this research project aims to combine these two lines of research, investigating new unsupervised learning methods that are based on ranking information and deep learning. In addition, it is also intended to investigate other retrieval and classification scenarios, expanding the domains of application of unsupervised learning methods. (AU)