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

This paper proposes a new lightweight selector to capture logit shift trends in Continual Learning (CL), a computationally expensive challenge in pre-trained model selection. The research addresses architectural heterogeneity in neural networks by decoupling architecture and data dependency to establish a new theoretical framework.