Continual learning, also known as incremental learning or life-long learning, stands at the
forefront of deep learning and AI systems. It breaks through the obstacle of one-way training …

This book chapter delves into the dynamics of continual learning, which is the process of
incrementally learning from a non-stationary stream of data. Although continual learning is a …

Lifelong learning portrays learning gradually in nonstationary environments and emulates
the process of human learning, which is efficient, robust, and able to learn new concepts …

To strike a delicate balance between model stability and plasticity of continual learning,
previous approaches have adopted strategies to guide model updates on new data to …

Deep learning models often suffer from catastrophic forgetting when fine-tuned with samples
of new classes. This issue becomes even more challenging when there is a domain shift …

Due to the powerful ability to acquire new knowledge and preserve previously learned
concepts from a dynamic data stream, continual learning has recently garnered substantial …

Few-shot class incremental learning illustrates the challenges of learning new concepts,
where the learner can access only a small sample per concept. The standard incremental …

Federated Named Entity Recognition (FNER) boosts model training within each local client
by aggregating the model updates of decentralized local clients, without sharing their private …

Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well
known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves …

Lifelong imitation learning for manipulation tasks poses significant challenges due to
distribution shifts that occur in incremental learning steps. Existing methods often focus on …