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 …

Abstract Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On
top of that, exemplar-free CIL is even more challenging due to forbidden access to data of …

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 (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 …

Continual learning (CL) provides a framework for training models in ever-evolving
environments. Although re-occurrence of previously seen objects or tasks is common in real …

Abstract Generalized Continual Category Discovery (GCCD) tackles learning from
sequentially arriving, partially labeled datasets while uncovering new categories. Traditional …

Humans have the ability to constantly learn new knowledge. However, for artificial
intelligence, trying to continuously learn new knowledge usually results in catastrophic …

Continual Learning (CL) aims to learn in non-stationary scenarios, progressively acquiring
and maintaining knowledge from sequential tasks. Recent Prompt-based Continual …

Clinical experts employ diverse philosophies and strategies in patient care, influenced by
regional patient populations. However, existing medical artificial intelligence (AI) models are …

The goal of few-shot class incremental learning (FSCIL) is to learn new concepts from a
limited number of novel samples while preserving the knowledge of previously learned …