Continual Learning has inspired a plethora of approaches and evaluation settings; however,
the majority of them overlooks the properties of a practical scenario, where the data stream …

Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously
acquired knowledge. However, recent CL advances are restricted to supervised continual …

Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …

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 …

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 …

Rehearsal approaches enjoy immense popularity with Continual Learning (CL)
practitioners. These methods collect samples from previously encountered data distributions …

Continual Learning (CL) methods aim to enable machine learning models to learn new
tasks without catastrophic forgetting of those that have been previously mastered. Existing …

In continual learning (CL), an agent learns from a stream of tasks leveraging prior
experience to transfer knowledge to future tasks. It is an ideal framework to decrease the …

In Continual Learning, a Neural Network is trained on a stream of data whose distribution
shifts over time. Under these assumptions, it is especially challenging to improve on classes …

Existing continual learning methods use Batch Normalization (BN) to facilitate training and
improve generalization across tasks. However, the non-iid and non-stationary nature of …