3 papers
cs.LG2026
Forgetting, plasticity, and co-observation: a third facet of continual learning
Timm Hess, Abhishek Jha, Gido M. van de Ven +1
Efficient continual learning remains a fundamental challenge for deep neural networks. While catastrophic forgetting and loss of plasticity are widely considered the primary obstac…
cs.LG2026
Position: Modular Memory is the Key to Continual Learning Agents
Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov +21
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these…
cs.CV2025
Forgetting of task-specific knowledge in model merging-based continual learning
Timm Hess, Gido M van de Ven, Tinne Tuytelaars
This paper investigates the linear merging of models in the context of continual learning (CL). Using controlled visual cues in computer vision experiments, we demonstrate that mer…