A major obstacle to developing artificial intelligence applications capable of true lifelong
learning is that artificial neural networks quickly or catastrophically forget previously learned …

Standard artificial neural networks suffer from the well-known issue of catastrophic
forgetting, making continual or lifelong learning difficult for machine learning. In recent years …

Artificial neural networks suffer from catastrophic forgetting. Unlike humans, when these
networks are trained on something new, they rapidly forget what was learned before. In the …

Continual learning, the setting where a learning agent is faced with a never-ending stream
of data, continues to be a great challenge for modern machine learning systems. In …

Abstract Replay strategies are Continual Learning techniques which mitigate catastrophic
forgetting by keeping a buffer of patterns from previous experiences, which are interleaved …

As image-based deep learning becomes pervasive on every device, from cell phones to
smart watches, there is a growing need to develop methods that continually learn from data …

Continual learning is an emerging research challenge in machine learning that addresses
the problem where models quickly fit the most recently trained-on data but suffer from …

Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic
forgetting (CF) problem. Existing research has achieved remarkable results in overcoming …

Continual learning requires the model to learn multiple tasks in a sequential order. To
perform continual learning, the model must possess the abilities to maintain performance on …

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 …