Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

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 …

Abstract According to Complementary Learning Systems (CLS) theory~\cite
{mcclelland1995there} in neuroscience, humans do effective\emph {continual learning} …

Humans and animals have the ability to continually acquire, fine-tune, and transfer
knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is …

People learn throughout life. However, incrementally updating conventional neural networks
leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the …

It was recently shown that architectural, regularization and rehearsal strategies can be used
to train deep models sequentially on a number of disjoint tasks without forgetting previously …

Replay is the reactivation of one or more neural patterns that are similar to the activation
patterns experienced during past waking experiences. Replay was first observed in …

Learning continuously during all model lifetime is fundamental to deploy machine learning
solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with …

Continual learning from a sequential stream of data is a crucial challenge for machine
learning research. Most studies have been conducted on this topic under the single-label …