Multi Stage

Multi-stage approaches are increasingly prevalent in machine learning, aiming to improve model performance and efficiency by breaking down complex tasks into sequential sub-tasks. Current research focuses on leveraging this strategy across diverse applications, employing architectures like two-stage neural networks, incorporating techniques such as retrieval augmentation and contrastive learning, and utilizing pre-trained models for transfer learning. This methodology offers significant advantages in handling high-dimensional data, mitigating computational costs, and enhancing accuracy, particularly in resource-constrained or low-data scenarios, with impacts spanning fields from image processing and natural language processing to medical diagnosis and robotic manipulation.