Federated learning (FL) is a hot collaborative training framework via aggregating model
parameters of decentralized local clients. However, most FL methods unreasonably assume …

Abstract Domain incremental learning aims to adapt to a sequence of domains with access
to only a small subset of data (ie, memory) from previous domains. Various methods have …

Although neural networks have been used extensively in pattern recognition scenarios, the
pre-acquisition of datasets is still challenging. In most pattern recognition areas, preparing a …

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is
gaining popularity due to its ability to enhance AIS performance, but the existing summaries …

Abstract Federated Class-Incremental Learning (FCIL) focuses on continually transferring
the previous knowledge to learn new classes in dynamic Federated Learning (FL). However …

Continual learning research has shown that neural networks suffer from catastrophic
forgetting" at the output level", but it is debated whether this is also the case at the level of …

Existing vision-based photovoltaic cell defect detection methods usually update models with
all defect data of both old and new categories to adapt to new classes emerging in the …

A pipeline of constant data streams is being built by the Internet of Things (IoT) to monitor
information about the physical environment. In parallel, Artificial Intelligence (AI) is …

Class-Incremental Learning (CIL) is a practical and challenging problem for achieving
general artificial intelligence. Recently, Pre-Trained Models (PTMs) have led to …

Contrastive representation learning has emerged as a promising technique for continual
learning as it can learn representations that are robust to catastrophic forgetting and …