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 …
Consistent Prompting for Rehearsal-Free Continual Learning
Continual learning empowers models to adapt autonomously to the ever-changing
environment or data streams without forgetting old knowledge. Prompt-based approaches …
environment or data streams without forgetting old knowledge. Prompt-based approaches …
Gradient reweighting: Towards imbalanced class-incremental learning
J He - Proceedings of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Abstract Class-Incremental Learning (CIL) trains a model to continually recognize new
classes from non-stationary data while retaining learned knowledge. A major challenge of …
classes from non-stationary data while retaining learned knowledge. A major challenge of …
Revisiting Neural Networks for Continual Learning: An Architectural Perspective
Efforts to overcome catastrophic forgetting have primarily centered around developing more
effective Continual Learning (CL) methods. In contrast, less attention was devoted to …
effective Continual Learning (CL) methods. In contrast, less attention was devoted to …
BSDP: Brain-inspired Streaming Dual-level Perturbations for Online Open World Object Detection
Y Chen, L Ma, L Jing, J Yu - Pattern Recognition, 2024 - Elsevier
Humans can easily distinguish the known and unknown categories and can recognize the
unknown object by learning it once instead of repeating it many times without forgetting the …
unknown object by learning it once instead of repeating it many times without forgetting the …