Architecture-driven Shift: towards a lightweight selector for capturing the trends of logit shift

1. 1Build a lightweight selector using architecture-driven shift
2. 2Run experiments to evaluate the selector's performance in capturing logit shift trends
3. 3Configure the selector to balance plasticity-stability in continual learning scenarios
4. 4Test the selector's ability to choose the best pre-trained model for a given task
5. 5Apply the selector to large-scale model selection to reduce computational cost

Researchers and AI engineers working on continual learning and model selection can benefit from this approach to improve model performance and efficiency

💡 A lightweight selector can capture logit shift trends, enabling efficient model selection and balancing plasticity-stability in continual learning

💡 Efficient model selection in continual learning using a lightweight logit shift selector