  Contrastive learning predicts whether two images belong to the same category
by training a model to make their feature representations as close or as far
away as possible. In this paper, we rethink how to mine samples in contrastive
learning, unlike other methods, our approach is more comprehensive, taking into
account both positive and negative samples, and mining potential samples from
two aspects: First, for positive samples, we consider both the augmented sample
views obtained by data augmentation and the mined sample views through data
mining. Then, we weight and combine them using both soft and hard weighting
strategies. Second, considering the existence of uninformative negative samples
and false negative samples in the negative samples, we analyze the negative
samples from the gradient perspective and finally mine negative samples that
are neither too hard nor too easy as potential negative samples, i.e., those
negative samples that are close to positive samples. The experiments show the
obvious advantages of our method compared with some traditional self-supervised
methods. Our method achieves 88.57%, 61.10%, and 36.69% top-1 accuracy on
CIFAR10, CIFAR100, and TinyImagenet, respectively.
