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Semi-Supervised Learning (SSL) with mismatched classes deals with the problemthat the classes-of-interests in the limited labeled data is only a subset ofthe classes in massive unlabeled data. As a result, the classes only possessedby the unlabeled data may mislead the classifier training and thus hinderingthe realistic landing of various SSL methods. To solve this problem, existingmethods usually divide unlabeled data to in-distribution (ID) data andout-of-distribution (OOD) data, and directly discard or weaken the OOD data toavoid their adverse impact. In other words, they treat OOD data as completelyuseless and thus the potential valuable information for classificationcontained by them is totally ignored. To remedy this defect, this paperproposes a "Transferable OOD data Recycling" (TOOR) method which properlyutilizes ID data as well as the "recyclable" OOD data to enrich the informationfor conducting class-mismatched SSL. Specifically, TOOR firstly attributes allunlabeled data to ID data or OOD data, among which the ID data are directlyused for training. Then we treat the OOD data that have a close relationshipwith ID data and labeled data as recyclable, and employ adversarial domainadaptation to project them to the space of ID data and labeled data. In otherwords, the recyclability of an OOD datum is evaluated by its transferability,and the recyclable OOD data are transferred so that they are compatible withthe distribution of known classes-of-interests. Consequently, our TOOR methodextracts more information from unlabeled data than existing approaches, so itcan achieve the improved performance which is demonstrated by the experimentson typical benchmark datasets.

They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning