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Learning Representations For Images With Hierarchical Labels
[article]
2020
arXiv
pre-print
Image classification has been studied extensively but there has been limited work in the direction of using non-conventional, external guidance other than traditional image-label pairs to train such models. In this thesis we present a set of methods to leverage information about the semantic hierarchy induced by class labels. In the first part of the thesis, we inject label-hierarchy knowledge to an arbitrary classifier and empirically show that availability of such external semantic
arXiv:2004.00909v2
fatcat:hxreipp6qzgszbbl6zpmlcsczy