Abstract The field of Continual Learning investigates the ability to learn consecutive tasks
without losing performance on those previously learned. The efforts of researchers have …

Despite the impressive performance in many individual tasks, deep neural networks suffer
from catastrophic forgetting when learning new tasks incrementally. Recently, various …

Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

Continual learning from a sequential stream of data is a crucial challenge for machine
learning research. Most studies have been conducted on this topic under the single-label …

Continual learning (CL) is a setting in which a model learns from a stream of incoming data
while avoiding to forget previously learned knowledge. Pre-trained language models (PLMs) …

The explosive growth of image data facilitates the fast development of image processing and
computer vision methods for emerging visual applications, meanwhile introducing novel …

Continually learning in the real world must overcome many challenges, among which noisy
labels are a common and inevitable issue. In this work, we present a replay-based continual …

For multimode processes, one generally establishes local monitoring models corresponding
to local modes. However, the significant features of previous modes may be catastrophically …

We emulate continual learning observed in real life, where new training data, which
represent new application domain, are used for gradual improvement of an Automatic …

Task incremental learning aims to enable a system to maintain its performance on
previously learned tasks while learning new tasks, solving the problem of catastrophic …