Rehearsal is a critical component for class-incremental continual learning, yet it requires a
substantial memory budget. Our work investigates whether we can significantly reduce this …

The focus of this study is on Unsupervised Continual Learning (UCL), as it presents an
alternative to Supervised Continual Learning which needs high-quality manual labeled data …

Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously
acquired knowledge. However, recent CL advances are restricted to supervised continual …

Continual learning usually assumes the incoming data are fully labeled, which might not be
applicable in real applications. In this work, we consider semi-supervised continual learning …

In continual learning (CL), an agent learns from a stream of tasks leveraging prior
experience to transfer knowledge to future tasks. It is an ideal framework to decrease the …

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

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

Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks
without incurring forgetting. CL settings proposed in literature assume that every incoming …

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …

We study continual learning in the large scale setting where tasks in the input sequence are
not limited to classification, and the outputs can be of high dimension. Among multiple state …