Feature Backbone Fine Tuning

Feature backbone fine-tuning refines pre-trained convolutional or transformer networks for specific downstream tasks, aiming to improve performance while mitigating issues like catastrophic forgetting and computational cost. Current research focuses on developing strategies like dynamic freezing of backbone layers and feature transformation tuning to optimize the balance between leveraging pre-trained knowledge and adapting to new data distributions, particularly in domains like remote sensing and autonomous driving. These advancements enhance the efficiency and effectiveness of deep learning models across diverse applications by enabling more targeted and adaptable feature extraction.