  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.
