Gradual Unfreezing

Gradual unfreezing is a machine learning technique that sequentially unlocks model parameters during training, improving performance and generalization by strategically controlling the flow of information. Current research focuses on applying this approach to various models, including transformer networks and faster R-CNN architectures, within diverse applications such as federated learning and few-shot object detection. This technique addresses challenges like catastrophic forgetting and client drift in federated learning, and enhances performance in scenarios with limited data, ultimately leading to more efficient and robust machine learning systems. The impact spans improved model accuracy across various domains and more efficient training processes.