Contrastive learning has been adopted as a core method for unsupervised
visual representation learning. Without human annotation, the common practice
is to perform an instance discrimination task: Given a query image crop, this
task labels crops from the same image as positives, and crops from other
randomly sampled images as negatives. An important limitation of this label
assignment strategy is that it can not reflect the heterogeneous similarity
between the query crop and each crop from other images, taking them as equally
negative, while some of them may even belong to the same semantic class as the
query. To address this issue, inspired by consistency regularization in
semi-supervised learning on unlabeled data, we propose Consistent Contrast
(CO2), which introduces a consistency regularization term into the current
contrastive learning framework. Regarding the similarity of the query crop to
each crop from other images as "unlabeled", the consistency term takes the
corresponding similarity of a positive crop as a pseudo label, and encourages
consistency between these two similarities. Empirically, CO2 improves Momentum
Contrast (MoCo) by 2.9% top-1 accuracy on ImageNet linear protocol, 3.8% and
1.1% top-5 accuracy on 1% and 10% labeled semi-supervised settings. It also
transfers to image classification, object detection, and semantic segmentation
on PASCAL VOC. This shows that CO2 learns better visual representations for
these downstream tasks.

该研究提出了一种新的无监督对比学习框架 CO2，它利用了模仿半监督学习中的一致性正则化来处理标签分配策略的瓶颈问题，能够提高图像分类、目标检测和语义分割等任务的视觉表示能力。