  Recently, learning from vast unlabeled data, especially self-supervised
learning, has been emerging and attracted widespread attention. Self-supervised
learning followed by the supervised fine-tuning on a few labeled examples can
significantly improve label efficiency and outperform standard supervised
training using fully annotated data. In this work, we present a novel
self-supervised deep learning paradigm based on online hard negative pair
mining. Specifically, we design a student-teacher network to generate
multi-view of the data for self-supervised learning and integrate hard negative
pair mining into the training. Then we derive a new triplet-like loss
considering both positive sample pairs and mined hard negative sample pairs.
Extensive experiments demonstrate the effectiveness of the proposed method and
its components on ILSVRC-2012.
