  To address semi-supervised learning from both labeled and unlabeled data, we
present a novel meta-learning scheme. We particularly consider that labeled and
unlabeled data share disjoint ground truth label sets, which can be seen tasks
like in person re-identification or image retrieval. Our learning scheme
exploits the idea of leveraging information from labeled to unlabeled data.
Instead of fitting the associated class-wise similarity scores as most
meta-learning algorithms do, we propose to derive semantics-oriented similarity
representations from labeled data, and transfer such representation to
unlabeled ones. Thus, our strategy can be viewed as a self-supervised learning
scheme, which can be applied to fully supervised learning tasks for improved
performance. Our experiments on various tasks and settings confirm the
effectiveness of our proposed approach and its superiority over the
state-of-the-art methods.
