Deep class-incremental learning: A survey
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 …
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
Foster: Feature boosting and compression for class-incremental learning
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 …
However, deep neural networks suffer from catastrophic forgetting when learning new …
Forward compatible few-shot class-incremental learning
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 …
authentication system, and a machine learning model should recognize new classes without …
A model or 603 exemplars: Towards memory-efficient class-incremental learning
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 …
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …
Few-shot class-incremental learning by sampling multi-phase tasks
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 …
media and new types of products in e-commerce. A model should recognize new classes …
Expandable subspace ensemble for pre-trained model-based class-incremental learning
Abstract Class-Incremental Learning (CIL) requires a learning system to continually learn
new classes without forgetting. Despite the strong performance of Pre-Trained Models …
new classes without forgetting. Despite the strong performance of Pre-Trained Models …
Fecam: Exploiting the heterogeneity of class distributions in exemplar-free continual learning
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 …
the rehearsal of data from previous tasks and thus suffers from catastrophic forgetting …
[PDF][PDF] Beef: Bi-compatible class-incremental learning via energy-based expansion and fusion
Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …
Learning without forgetting for vision-language models
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 …
world, which requires a learning system to adapt to new tasks without forgetting former ones …
Dynamic residual classifier for class incremental learning
X Chen, X Chang - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
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 …
incremental learning (CIL) by preserving limited exemplars from previous tasks. With …