Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …

We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL)
algorithms through the metric of online accuracy, which measures the accuracy of the model …

Continual learning in online scenario aims to learn a sequence of new tasks from data
stream using each data only once for training, which is more realistic than in offline mode …

Online continual learning (CL) studies the problem of learning continuously from a single-
pass data stream while adapting to new data and mitigating catastrophic forgetting …

Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …

In continual learning, a system must incrementally learn from a non-stationary data stream
without catastrophic forgetting. Recently, multiple methods have been devised for …

Targeted for real world scenarios, online continual learning aims to learn new tasks from
sequentially available data under the condition that each data is observed only once by the …

Recent years have seen significant progress in unsupervised continual learning methods.
Despite their success in controlled settings, their practicality in real-world contexts remains …

Online class-incremental continual learning aims to learn new classes continually from a
never-ending and single-pass data stream, while not forgetting the learned knowledge of old …

Continual learning is the problem of learning and retaining knowledge through time over
multiple tasks and environments. Research has primarily focused on the incremental …