Two Stage Deep Learning

Two-stage deep learning approaches are increasingly used to solve complex problems by breaking them down into simpler, sequential sub-tasks. Current research focuses on applications ranging from medical image analysis (e.g., lesion segmentation, quality assessment) and remote sensing (e.g., object detection, damage assessment) to more abstract problems like sequential decision-making and model reduction. Common architectures employed include variations of U-Net, CNNs, and recurrent networks, often combined with techniques like model ensembling, adversarial training, and physics-informed neural networks to improve accuracy and robustness. This methodology offers significant advantages in handling intricate data and improving the efficiency and accuracy of solutions across diverse scientific and engineering domains.