论文标题
广义参数对比度学习
Generalized Parametric Contrastive Learning
论文作者
论文摘要
在本文中，我们提出了广义参数对比度学习（GPACO/PACO），该学习在不平衡和平衡数据上都很好。基于理论分析，我们观察到，受监督的对比损失倾向于偏向高频类别，从而增加了学习不平衡的学习难度。我们从优化的角度介绍了一组参数级别的可学习中心，以重新平衡。此外，我们在平衡的环境下分析了GPACO/PACO损失。我们的分析表明，GPACO/PACO可以适应性地增强同一类样品的强度，因为将更多的样品与相应的中心一起拉在一起并有益于艰难的例子学习。长尾基准测试的实验表明了长尾识别的新最先进。在完整的Imagenet上，与MAE模型相比，从CNN到接受GPACO损失训练的视觉变压器的模型显示出更好的泛化性能和更强的鲁棒性。此外，GPACO可以应用于语义分割任务，并在4个最受欢迎的基准测试中观察到明显的改进。我们的代码可在https://github.com/dvlab-research/parametric-contrastive-learning上找到。
In this paper, we propose the Generalized Parametric Contrastive Learning (GPaCo/PaCo) which works well on both imbalanced and balanced data. Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our GPaCo/PaCo loss under a balanced setting. Our analysis demonstrates that GPaCo/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 benchmarks manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models from CNNs to vision transformers trained with GPaCo loss show better generalization performance and stronger robustness compared with MAE models. Moreover, GPaCo can be applied to the semantic segmentation task and obvious improvements are observed on the 4 most popular benchmarks. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.
