13 papers
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…
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…
Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability
Sergi Masip, Gido M. van de Ven, Javier Ferrando +1
Catastrophic forgetting in continual learning is often measured at the performance or last-layer representation level, overlooking the underlying mechanisms. We introduce a mechani…
Cross-Modal Prototype Alignment and Mixing for Training-Free Few-Shot Classification
Dipam Goswami, Simone Magistri, Gido M. van de Ven +4
Vision-language models (VLMs) like CLIP are trained with the objective of aligning text and image pairs. To improve CLIP-based few-shot image classification, recent works have obse…
Revisiting Weight Regularization for Low-Rank Continual Learning
Yaoyue Zheng, Yin Zhang, Joost van de Weijer +4
Continual Learning (CL) with large-scale pre-trained models (PTMs) has recently gained wide attention, shifting the focus from training from scratch to continually adapting PTMs. T…
Learning continually with representational drift
Suzanne van der Veldt, Gido M. van de Ven, Sanne Moorman +1
Deep artificial neural networks famously struggle to learn from non-stationary streams of data. Without dedicated mitigation strategies, continual learning is associated with conti…