Abstract Incremental learning using Deep Neural Networks (DNNs) suffers from catastrophic
forgetting. Existing methods mitigate it by either storing old image examples or only updating …

The ability to learn new concepts continually is necessary in this ever-changing world.
However, deep neural networks suffer from catastrophic forgetting when learning new …

Deep neural network (DNN) suffers from catastrophic forgetting when learning
incrementally, which greatly limits its applications. Although maintaining a handful of …

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 …

Abstract Class Incremental Learning (CIL) aims to sequentially learn new classes while
avoiding catastrophic forgetting of previous knowledge. We propose to use Masked …

In class-incremental learning, a learning agent faces a stream of data with the goal of
learning new classes while not forgetting previous ones. Neural networks are known to …

We propose a causal framework to explain the catastrophic forgetting in Class-Incremental
Learning (CIL) and then derive a novel distillation method that is orthogonal to the existing …

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

We introduce an approach for incremental learning that preserves feature descriptors of
training images from previously learned classes, instead of the images themselves, unlike …

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 …