Dropout Method

Dropout is a regularization technique used in neural networks to prevent overfitting and improve generalization by randomly ignoring neurons during training. Current research focuses on adapting dropout methods for various architectures, including transformers and large language models, and exploring structured dropout approaches that selectively drop entire blocks or dimensions rather than individual neurons, as well as applying dropout within federated learning frameworks to improve communication efficiency. These advancements enhance model robustness, particularly in scenarios with limited data or heterogeneous environments, leading to improved performance in diverse applications such as image segmentation, natural language processing, and recommendation systems.