  Semi-Supervised Learning (SSL) with mismatched classes deals with the problem
that the classes-of-interests in the limited labeled data is only a subset of
the classes in massive unlabeled data. As a result, the classes only possessed
by the unlabeled data may mislead the classifier training and thus hindering
the realistic landing of various SSL methods. To solve this problem, existing
methods usually divide unlabeled data to in-distribution (ID) data and
out-of-distribution (OOD) data, and directly discard or weaken the OOD data to
avoid their adverse impact. In other words, they treat OOD data as completely
useless and thus the potential valuable information for classification
contained by them is totally ignored. To remedy this defect, this paper
proposes a "Transferable OOD data Recycling" (TOOR) method which properly
utilizes ID data as well as the "recyclable" OOD data to enrich the information
for conducting class-mismatched SSL. Specifically, TOOR firstly attributes all
unlabeled data to ID data or OOD data, among which the ID data are directly
used for training. Then we treat the OOD data that have a close relationship
with ID data and labeled data as recyclable, and employ adversarial domain
adaptation to project them to the space of ID data and labeled data. In other
words, the recyclability of an OOD datum is evaluated by its transferability,
and the recyclable OOD data are transferred so that they are compatible with
the distribution of known classes-of-interests. Consequently, our TOOR method
extracts more information from unlabeled data than existing approaches, so it
can achieve the improved performance which is demonstrated by the experiments
on typical benchmark datasets.
