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 …

Continual learning (CL) is a learning paradigm that emulates the human capability of
learning and accumulating knowledge continually without forgetting the previously learned …

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 …

Large language models (LLMs) have been transformative. They are pretrained foundational
models that are self-supervised and can be adapted with fine-tuning to a wide range of …

Popular deep-learning models in the field of image classification suffer from catastrophic
forgetting—models will forget previously acquired skills when learning new ones …

Abstract The field of Continual Learning investigates the ability to learn consecutive tasks
without losing performance on those previously learned. The efforts of researchers have …

Real-time on-device continual learning is needed for new applications such as home robots,
user personalization on smartphones, and augmented/virtual reality headsets. However, this …

Lifelong learning—an agent's ability to learn throughout its lifetime—is a hallmark of
biological learning systems and a central challenge for artificial intelligence (AI). The …

Artificial neural networks are known to suffer from catastrophic forgetting: when learning
multiple tasks sequentially, they perform well on the most recent task at the expense of …

In continual learning, a system must incrementally learn from a non-stationary data stream
without catastrophic forgetting. Recently, multiple methods have been devised for …