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 …

Continual learning aims to enable a single model to learn a sequence of tasks without
catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store …

Modern computer vision applications suffer from catastrophic forgetting when incrementally
learning new concepts over time. The most successful approaches to alleviate this forgetting …

Continual learning (CL) is widely regarded as crucial challenge for lifelong AI. However,
existing CL benchmarks, eg Permuted-MNIST and Split-CIFAR, make use of artificial …

This work investigates the entanglement between Continual Learning (CL) and Transfer
Learning (TL). In particular, we shed light on the widespread application of network …

Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of
safety assurance. However objects encountered on the road exhibit a long-tailed distribution …

This paper studies class incremental learning (CIL) of continual learning (CL). Many
approaches have been proposed to deal with catastrophic forgetting (CF) in CIL. Most …

The paradigm of machine intelligence moves from purely supervised learning to a more
practical scenario when many loosely related unlabeled data are available and labeled data …

Despite significant advances, the performance of state-of-the-art continual learning
approaches hinges on the unrealistic scenario of fully labeled data. In this paper, we tackle …

Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine
learning where the usual assumption of stationary data distribution is relaxed or omitted …