Fly-CL: A Fly-Inspired Framework for Enhancing Efficient Decorrelation and Reduced Training Time in Pre-trained Model-based Continual Representation Learning

Researchers introduce Fly-CL, a bio-inspired framework for continual representation learning that significantly reduces training time while maintaining performance comparable to state-of-the-art methods. The approach, inspired by fly olfactory circuits, addresses multicollinearity issues in pre-trained models and enables more efficient similarity matching for real-time applications.