This paper introduces a continual learning approach named MagMax, which utilizes model
merging to enable large pre-trained models to continuously learn from new data without …

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 …

In this work, we study the problem of continual learning (CL) where the goal is to learn a
model on a sequence of tasks, under the assumption that the data from the previous tasks …

Continual learning strives to acquire knowledge across sequential tasks without forgetting
previously assimilated knowledge. Current state-of-the-art methodologies utilize dynamic …

Background: Self-supervised pre-training of deep learning models with contrastive learning
is a widely used technique in image analysis. Current findings indicate a strong potential for …

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 …