  Learning under a continuously changing data distribution with incorrect
labels is a desirable real-world problem yet challenging. A large body of
continual learning (CL) methods, however, assumes data streams with clean
labels, and online learning scenarios under noisy data streams are yet
underexplored. We consider a more practical CL task setup of an online learning
from blurry data stream with corrupted labels, where existing CL methods
struggle. To address the task, we first argue the importance of both diversity
and purity of examples in the episodic memory of continual learning models. To
balance diversity and purity in the episodic memory, we propose a novel
strategy to manage and use the memory by a unified approach of label noise
aware diverse sampling and robust learning with semi-supervised learning. Our
empirical validations on four real-world or synthetic noise datasets (CIFAR10
and 100, mini-WebVision, and Food-101N) exhibit that our method significantly
outperforms prior arts in this realistic and challenging continual learning
scenario. Code and data splits are available in
https://github.com/clovaai/puridiver.
