Deep models have shown to be vulnerable to catastrophic forgetting, a phenomenon that
the recognition performance on old data degrades when a pre-trained model is fine-tuned …

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

We present a novel class incremental learning approach based on deep neural networks,
which continually learns new tasks with limited memory for storing examples in the previous …

Deep neural networks are prone to catastrophic forgetting when incrementally trained on
new classes or new tasks as adaptation to the new data leads to a drastic decrease of the …

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 …

Non-exemplar class-incremental learning (NECIL) requires deep models to maintain
existing knowledge while continuously learning new classes without saving old class …

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 …

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 …

Abstract Class-Incremental Learning (CIL) aims to build classification models from data
streams. At each step of the CIL process, new classes must be integrated into the model …