Dropout Layer

Dropout layers are a regularization technique in neural networks that randomly ignore neurons during training, improving model generalization and preventing overfitting. Current research explores variations like layer-wise and channel-wise dropout, integrating dropout into different model architectures (e.g., transformers, diffusion models) and employing it for uncertainty quantification and robust training in resource-constrained environments. These advancements enhance model performance, robustness, and efficiency across diverse applications, including natural language processing, image classification, and multi-sensor fusion.