To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …

Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …

Abstract Tangent Model Composition (TMC) is a method to combine component models
independently fine-tuned around a pre-trained point. Component models are tangent …

Continual learning enables incremental learning of new tasks without forgetting those
previously learned, resulting in positive knowledge transfer that can enhance performance …

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 …

Deep Learning models have achieved remarkable performance in tasks such as image
classification or generation, often surpassing human accuracy. However, they can struggle …

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 …

Incremental Learning (IL) aims to develop Machine Learning (ML) models that can learn
from continuous streams of data and mitigate catastrophic forgetting. We analyse the current …