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

Continual Learning (CL) aims to develop agents emulating the human ability to sequentially
learn new tasks while being able to retain knowledge obtained from past experiences. In this …

Continual learning methods aim at training a neural network from sequential data with
streaming labels, relieving catastrophic forgetting. However, existing methods are based on …

With the success of pretraining techniques in representation learning, a number of continual
learning methods based on pretrained models have been proposed. Some of these …

Continual (sequential) training and multitask (simultaneous) training are often attempting to
solve the same overall objective: to find a solution that performs well on all considered tasks …

Continual Learning aims to learn a single model on a sequence of tasks without having
access to data from previous tasks. The biggest challenge in the domain still remains …

Continual Learning (CL) research typically focuses on tackling the phenomenon of
catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an …

Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in
a non-stationary data stream without forgetting old ones. Most CL works focus on tackling …

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 …