Multiple Deep Neural Network

Multiple deep neural network (DNN) research focuses on improving the performance, efficiency, and applicability of using multiple DNNs together for various tasks. Current efforts concentrate on optimizing ensemble methods, including exploring diverse architectures like ResNet, InceptionNet, and VGG, and developing novel training strategies such as federated learning and adaptive resource allocation for multi-DNN inference on resource-constrained devices. This research is significant because it addresses challenges in accuracy, computational cost, and fairness inherent in single DNN models, leading to advancements in diverse fields like remote sensing, solar physics, and medical image analysis.