When an agent acquires new information, ideally it would immediately be capable of using
that information to understand its environment. This is not possible using conventional deep …

Learning from non-stationary data streams and overcoming catastrophic forgetting still
poses a serious challenge for machine learning research. Rather than aiming to improve …

Although deep learning approaches have stood out in recent years due to their state-of-the-
art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall …

Conventionally, deep neural networks are trained offline, relying on a large dataset
prepared in advance. This paradigm is often challenged in real-world applications, eg online …

Continually learning in the real world must overcome many challenges, among which noisy
labels are a common and inevitable issue. In this work, we present a replay-based continual …

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

Popular deep-learning models in the field of image classification suffer from catastrophic
forgetting—models will forget previously acquired skills when learning new ones …

Continual learning, the setting where a learning agent is faced with a never-ending stream
of data, continues to be a great challenge for modern machine learning systems. In …

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 …

Lifelong learning with deep neural networks is well-known to suffer from catastrophic
forgetting: the performance on previous tasks drastically degrades when learning a new …