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

Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast