In class incremental learning (CIL) a model must learn new classes in a sequential manner
without forgetting old ones. However, conventional CIL methods consider a balanced …

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

Plasticity-stability dilemma is a main problem for incremental learning, where plasticity is
referring to the ability to learn new knowledge, and stability retains the knowledge of …

Although deep learning approaches have stood out in recent years due to their state-of-the-
art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall …

Class incremental learning (CIL) enables deep networks to progressively learn new tasks
while remembering previously learned knowledge. A popular design for CIL involves …

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

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 …

Incremental learning is useful if an AI agent needs to integrate data from a stream. The
problem is non trivial if the agent runs on a limited computational budget and has a bounded …

Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …

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 …