Beyond Backbone Backpropagation: A Decoupled Strategy for Efficient Transfer Learning
A new transfer learning strategy decouples feature extraction from classifier optimization to reduce computational overhead.
Researchers propose a lightweight training method that adapts only the normalization layers of a model to new domains. By precomputing features once and using a redesigned margin-based classifier head, the approach significantly lowers energy and compute requirements compared to standard backpropagation-based fine-tuning.