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 …

In the online continual learning paradigm, agents must learn from a changing distribution
while respecting memory and compute constraints. Experience Replay (ER), where a small …

Prior work on continual learning often operate in a``task-aware" manner, by assuming that
the task boundaries and identifies of the data instances are known at all times. While in …

While recent continual learning methods largely alleviate the catastrophic problem on toy-
sized datasets, some issues remain to be tackled to apply them to real-world problem …

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 …

Despite rapid advances in continual learning, a large body of research is devoted to
improving performance in the existing setups. While a handful of work do propose new …

Inspired by human intelligence, the research on online continual learning aims to push the
limits of the machine learning models to constantly learn from sequentially encountered …

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 entails learning a sequence of tasks and balancing their knowledge
appropriately. With limited access to old training samples, much of the current work in deep …

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 …