The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …

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 …

Continual learning is a subfield of machine learning, which aims to allow machine learning
models to continuously learn on new data, by accumulating knowledge without forgetting …

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 …

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real
world, which requires a learning system to adapt to new tasks without forgetting former ones …

While traditional machine learning can effectively tackle a wide range of problems, it
primarily operates within a closed-world setting, which presents limitations when dealing …

One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

Continual learning (CL) trains NN models incrementally from a continuous stream of tasks.
To remember previously learned knowledge, prior studies store old samples over a memory …