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This paper proposes integrating semantics-oriented similarity representationinto RankingMatch, a recently proposed semi-supervised learning method. Ourmethod, dubbed ReRankMatch, aims to deal with the case in which labeled andunlabeled data share non-overlapping categories. ReRankMatch encourages themodel to produce the similar image representations for the samples likelybelonging to the same class. We evaluate our method on various datasets such asCIFAR-10, CIFAR-100, SVHN, STL-10, and Tiny ImageNet. We obtain promisingresults (4.21% error rate on CIFAR-10 with 4000 labels, 22.32% error rate onCIFAR-100 with 10000 labels, and 2.19% error rate on SVHN with 1000 labels)when the amount of labeled data is sufficient to learn semantics-orientedsimilarity representation. The code is made publicly available athttps://github.com/tqtrunghnvn/ReRankMatch.

ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation