Recently, contrastive learning has shown its effectiveness in self-supervised learning by training features of augmentations of input images based on the contrastive loss. This paper aims to introduce contrastive learning and discuss the effects of augmentations, features, and parameters of contrastive learning. Interactive figures are developed to demonstrate the effective schemes proposed in contrastive learning and can be accessed on IEEE Xplore.

中文翻译：

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玩转蒙特卡罗树搜索 [AI-eXplained]

最近，对比学习通过基于对比损失训练输入图像的增强特征，显示了其在自监督学习中的有效性。本文旨在介绍对比学习并讨论对比学习的增强、特征和参数的效果。开发了交互式图形来演示对比学习中提出的有效方案，并且可以在 IEEE Xplore 上访问。