Self-supervised learning has become a popular approach in recent years for its ability to learn meaningful representations without the need for data annotation. This paper proposes a novel image augmentation technique, overlaying images, which has not been widely applied in self-supervised learning. This method is designed to provide better guidance for the model to understand underlying information, resulting in more useful representations. The proposed method is evaluated using contrastive learning, a widely used self-supervised learning method that has shown solid performance in downstream tasks. The results demonstrate the effectiveness of the proposed augmentation technique in improving the performance of self-supervised models.

翻译：近年来,自我监督的学习已成为一种流行的做法,因为它有能力在不需要数据注释的情况下学习有意义的表述,本文件提出了一种新的图像增强技术,即覆盖图像技术,在自我监督的学习中尚未广泛应用。这一方法旨在为模型提供更好的指导,以了解基本信息,从而产生更有用的表述。拟议方法使用对比式学习进行评估,这是一种广泛使用的自我监督的学习方法,在下游任务中表现良好。结果显示了拟议中的增强技术在改进自监督模式绩效方面的有效性。