Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …

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 …

Novel classes frequently arise in our dynamically changing world, eg, new users in the
authentication system, and a machine learning model should recognize new classes without …

Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

New classes arise frequently in our ever-changing world, eg, emerging topics in social
media and new types of products in e-commerce. A model should recognize new classes …

Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …

Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …

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

Class-Incremental Learning (CIL) or continual learning is a desired capability in the real
world, which requires a learning system to adapt to new tasks without forgetting former ones …

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 …