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The ability to continuously acquire new knowledge without forgetting previous tasks remains a challenging problem for computer vision systems. Standard continual learning benchmarks focus on learning from static iid images in an offline setting. Here, we examine a more challenging and realistic online continual learning problem called online stream learning. Like humans, some AI agents have to learn incrementally from a continuous temporal stream of non-repeating data. We propose a novel model,arXiv:2104.02206v4 fatcat:2yi3jeo32ndnno2cs6m7tr76ka