Deep neural networks (DNNs) have been applied in class incremental learning, which aims
to solve common real-world problems of learning new classes continually. One drawback of …

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 …

Modern machine learning suffers from catastrophic forgetting when learning new classes
incrementally. The performance dramatically degrades due to the missing data of old …

Contemporary neural networks are limited in their ability to learn from evolving streams of
training data. When trained sequentially on new or evolving tasks, their accuracy drops …

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 …

Abstract Class Incremental Learning (CIL) aims at learning a classifier in a phase-by-phase
manner, in which only data of a subset of the classes are provided at each phase. Previous …

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 …

A well-known issue for class-incremental learning is the catastrophic forgetting
phenomenon, where the network's recognition performance on old classes degrades …

Modern deep learning approaches have achieved great success in many vision applications
by training a model using all available task-specific data. However, there are two major …

The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class
incremental learning (CIL) by preserving limited exemplars from previous tasks. With …