The ability of artificial agents to increment their capabilities when confronted with new data is
an open challenge in artificial intelligence. The main challenge faced in such cases is …

Exemplar-free class-incremental learning is very challenging due to the negative effect of
catastrophic forgetting. A balance between stability and plasticity of the incremental process …

This paper presents a class incremental learning (IL) method which exploits fine tuning and
a dual memory to reduce the negative effect of catastrophic forgetting in image recognition …

People learn throughout life. However, incrementally updating conventional neural networks
leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the …

Study Design: Narrative review. Objectives: This review aims to present current applications
of machine learning (ML) in spine domain to clinicians. Methods: We conducted a …

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …

Recognizing visual unseen classes, ie for which no training data is available, is known as
Zero Shot Learning (ZSL). Some of the best performing methods apply the triplet loss to …

With the memory-resource-limited constraints, class-incremental learning (CIL) usually
suffers from the “catastrophic forgetting” problem when updating the joint classification …

As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a
sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and …

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