Adaptive Freezing

Adaptive freezing, a technique involving selectively freezing or unfreezing parts of a neural network during training, aims to improve model efficiency and generalization. Current research focuses on applying this strategy to various tasks, including continual learning, object detection, and Parkinson's disease diagnosis, often employing transformer networks, convolutional neural networks, and ensemble methods like BagStacking. This approach offers significant potential for reducing computational costs and improving performance in resource-constrained environments and for handling data variability in challenging applications. The resulting models are often more robust and efficient, leading to advancements in both theoretical understanding and practical applications.