Contrastive learning (CL) methods effectively learn data representations
without label supervision, where the encoder contrasts each positive sample
over multiple negative samples via a one-vs-many softmax cross-entropy loss. By
leveraging large amounts of unlabeled image data, recent CL methods have
achieved promising results when pre-trained on ImageNet, a well-curated data
set with balanced image classes. However, they tend to yield worse performance
when pre-trained on images in the wild. In this paper, to further improve the
performance of CL and enhance its robustness on uncurated data sets, we propose
a doubly CL strategy that contrasts the positive (negative) samples of a query
within themselves before deciding how strongly to pull (push) them. We realize
this strategy with contrastive attraction and contrastive repulsion (CACR),
which makes the query not only exert a greater force to attract more distant
positive samples but also do so to repel closer negative samples. Theoretical
analysis reveals that CACR generalizes CL's behavior by taking into
consideration the differences between the distributions of the
positive/negative samples, which in general are sampled independently of the
query, and their true conditional distributions given the query. We demonstrate
this unique intra-positive attraction and intra-negative repulsion mechanism,
which helps remove the need to assume uniform prior distributions on both the
data and their latent representation, is particularly beneficial when data sets
are less curated. Extensive large-scale experiments on a number of standard
vision tasks show that CACR not only consistently outperforms existing CL
methods on benchmark data sets in representation learning, but also shows
better robustness when generalized to pre-training on imbalanced image data
sets.

本文提出一种双重对比学习策略（CACR），利用对比吸引和对比排斥机制，在无监督数据集上实现图像表示的改进和鲁棒性提高，特别是在数据集不太平衡时表现良好。