A primary goal of class-incremental learning is to strike a balance between stability and
plasticity, where models should be both stable enough to retain knowledge learned from …

Class-incremental learning (CIL) aims at continuously updating a trained model with new
classes (plasticity) without forgetting previously learned old ones (stability). Contemporary …

Abstract Class-Incremental Learning (CIL) aims to learn a classification model with the
number of classes increasing phase-by-phase. An inherent problem in CIL is the stability …

In class-incremental learning, the model is expected to learn new classes continually while
maintaining knowledge on previous classes. The challenge here lies in preserving the …

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 …

Non-exemplar class-incremental learning is to recognize both the old and new classes
when old class samples cannot be saved. It is a challenging task since representation …

Class-incremental learning (CIL) has been widely studied under the setting of starting from a
small number of classes (base classes). Instead, we explore an understudied real-world …

Non-exemplar class-incremental learning aims to recognize both the old and new classes
without access to old class samples. The conflict between old and new class optimization is …

Deep neural networks (DNNs) often suffer from" catastrophic forgetting" during incremental
learning (IL)---an abrupt degradation of performance on the original set of classes when the …

Abstract In Class-Incremental Learning (CIL) an image classification system is exposed to
new classes in each learning session and must be updated incrementally. Methods …