Continual Learning (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …

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 …

The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting is the central …

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

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 (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 is a setting where machine learning models learn novel concepts from
continuously shifting training data, while simultaneously avoiding degradation of knowledge …

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 …

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 …

Online continual learning (CL) studies the problem of learning continuously from a single-
pass data stream while adapting to new data and mitigating catastrophic forgetting …