After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive learning has demonstrated its great potential in learning from unlabeled data. However, the online input data are usually none independent and identically distributed (non-iid) and storages of edge devices are usually too limited to store enough representative data from different data classes. We propose a framework to automatically select the most representative data from the unlabeled input stream, which only requires a small data buffer for dynamic learning. Experiments show that accuracy and learning speed are greatly improved.

翻译：在边缘设备上安装了模型后,这些设备最好能从未贴标签的数据中学习,以便不断提高准确性。对比性学习表明其在学习未贴标签数据方面具有巨大的潜力。然而,在线输入数据通常不是独立和同样分布的(非二d),边缘设备的储存通常过于有限,无法储存来自不同数据类别的足够有代表性的数据。我们建议了一个框架,以便从未贴标签的输入流中自动选择最具代表性的数据,这只需要一个小的数据缓冲来进行动态学习。实验显示,准确性和学习速度大为改善。