Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective
arXiv:2605.09044v1 Announce Type: new
Abstract: Deep continual learning requires models to adapt to new tasks without retraining from scratch. However, neural networks can lose their ability to adapt to new tasks after training on previous ones, a phe…