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

Continual learning has received a great deal of attention recently with several approaches
being proposed. However, evaluations involve a diverse set of scenarios making meaningful …

Artificial neural networks have exceeded human-level performance in accomplishing
several individual tasks (eg voice recognition, object recognition, and video games) …

Continual learning (CL) incrementally learns a sequence of tasks while solving the
catastrophic forgetting (CF) problem. Existing methods mainly try to deal with CF directly. In …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Experiments used in current continual learning research do not faithfully assess
fundamental challenges of learning continually. Instead of assessing performance on …

Continual learning (CL) learns a sequence of tasks incrementally. There are two popular CL
settings, class incremental learning (CIL) and task incremental learning (TIL). A major …

A large body of research in continual learning is devoted to overcoming the catastrophic
forgetting of neural networks by designing new algorithms that are robust to the distribution …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
where the network resembles a static entity of knowledge, acquired through generalized …