Fetril: Feature translation for exemplar-free class-incremental learning
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 …
catastrophic forgetting. A balance between stability and plasticity of the incremental process …
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 …
Striking a balance between stability and plasticity for class-incremental learning
Class-incremental learning (CIL) aims at continuously updating a trained model with new
classes (plasticity) without forgetting previously learned old ones (stability). Contemporary …
classes (plasticity) without forgetting previously learned old ones (stability). Contemporary …
Self-organizing pathway expansion for non-exemplar class-incremental learning
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 …
without access to old class samples. The conflict between old and new class optimization is …
Class-incremental learning with strong pre-trained models
Class-incremental learning (CIL) has been widely studied under the setting of starting from a
small number of classes (base classes). Instead, we explore an understudied real-world …
small number of classes (base classes). Instead, we explore an understudied real-world …
Self-sustaining representation expansion for non-exemplar class-incremental learning
Non-exemplar class-incremental learning is to recognize both the old and new classes
when old class samples cannot be saved. It is a challenging task since representation …
when old class samples cannot be saved. It is a challenging task since representation …
Class-incremental learning with cross-space clustering and controlled transfer
In class-incremental learning, the model is expected to learn new classes continually while
maintaining knowledge on previous classes. The challenge here lies in preserving the …
maintaining knowledge on previous classes. The challenge here lies in preserving the …
[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 …
Class-incremental learning via deep model consolidation
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 …
learning (IL)---an abrupt degradation of performance on the original set of classes when the …
On the stability-plasticity dilemma of class-incremental learning
A primary goal of class-incremental learning is to strike a balance between stability and
plasticity, where models should be both stable enough to retain knowledge learned from …
plasticity, where models should be both stable enough to retain knowledge learned from …