Online class-incremental continual learning is a specific task of continual learning. It aims to
continuously learn new classes from data stream and the samples of data stream are seen …

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 …

Online class-incremental continual learning (CL) studies the problem of learning new
classes continually from an online non-stationary data stream, intending to adapt to new …

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 …

Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the
only challenge. This paper argues for another challenge in class-incremental learning (CIL) …

Online continual learning (online CL) studies the problem of learning sequential tasks from
an online data stream without task boundaries, aiming to adapt to new data while alleviating …

The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting is the central …

Continual learning aims to learn on non-stationary data streams without catastrophically
forgetting previous knowledge. Prevalent replay-based methods address this challenge by …

As image-based deep learning becomes pervasive on every device, from cell phones to
smart watches, there is a growing need to develop methods that continually learn from data …

Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …