In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalance learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our PaCo loss under a balanced setting. Our analysis demonstrates that PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models trained with PaCo loss surpass supervised contrastive learning across various ResNet backbones. Our code is available at \url{https://github.com/jiequancui/Parametric-Contrastive-Learning}.

本文提出Parametric Contrastive Learning（PaCo）来解决长尾识别问题，通过引入一组类内可学习参数来重新平衡优化，PaCo可以自适应地提高将同类样本推向一起的强度，并造福于难度样本学习。实验表明，使用PaCo loss在长尾CIFAR，ImageNet，Places和iNaturalist 2018上具有新的领先水平。

参数对比学习