Online continual learning suffers from an underfitted solution due to insufficient training for
prompt model updates (eg single-epoch training). To address the challenge we propose an …

Ensuring the reliability of power electronic converters is a matter of great importance, and
data-driven condition monitoring techniques are cementing themselves as an important tool …

Existing research has shown that large language models (LLMs) exhibit remarkable
performance in language understanding and generation. However, when LLMs are …

Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior
knowledge while learning from limited new data streams, all without overfitting. The rise of …

Continual learning (CL) has garnered significant attention because of its ability to adapt to
new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a …

Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing
on enabling neural networks to sequentially learn tasks without explicit label information …

Continual learning aims to enable machine learning models to acquire new knowledge from
a shifting data distribution without forgetting what has already been learned. Such shifting …

In online CL, new data continuously arrive in a stream rather than in a large chunk (eg, task
unit). Several previous works [2][5] train the model only after a large chunk of new data …