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 …

Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …

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

Online class-incremental continual learning aims to learn new classes continually from a
never-ending and single-pass data stream, while not forgetting the learned knowledge of old …

Abstract Current evaluations of Continual Learning (CL) methods typically assume that there
is no constraint on training time and computation. This is an unrealistic assumption for any …

Continual learning is the problem of learning and retaining knowledge through time over
multiple tasks and environments. Research has primarily focused on the incremental …

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 …

We propose a general, yet simple patch that can be applied to existing regularization-based
continual learning methods called classifier-projection regularization (CPR). Inspired by …

We discuss a general formulation for the Continual Learning (CL) problem for classification—
a learning task where a stream provides samples to a learner and the goal of the learner …

In the setting of continual learning, a network is trained on a sequence of tasks, and suffers
from catastrophic forgetting. To balance plasticity and stability of network in continual …