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 …

Abstract Convolutional Neural Networks experience catastrophic forgetting when optimized
on a sequence of learning problems: as they meet the objective of the current training …

Continual learning is a longstanding research topic due to its crucial role in tackling
continually arriving tasks. Up to now, the study of continual learning in computer vision is …

Online class-incremental continual learning (CL) studies the problem of learning new
classes continually from an online non-stationary data stream, intending to adapt to new …

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 …

Traditional online continual learning (OCL) research has primarily focused on mitigating
catastrophic forgetting with fixed and limited storage allocation throughout an agent's …

While traditional machine learning can effectively tackle a wide range of problems, it
primarily operates within a closed-world setting, which presents limitations when dealing …

What is the state of the art in continual machine learning? Although a natural question for
predominant static benchmarks, the notion to train systems in a lifelong manner entails a …

This paper argues that continual learning methods can benefit by splitting the capacity of the
learner across multiple models. We use statistical learning theory and experimental analysis …

A large body of research in continual learning is devoted to overcoming the catastrophic
forgetting of neural networks by designing new algorithms that are robust to the distribution …