Two Stage Training

Two-stage training is a machine learning technique that divides the training process into two sequential phases, often aiming to improve model performance, efficiency, or robustness. Current research focuses on applying this approach to diverse areas, including natural language processing (e.g., question answering, machine translation), computer vision (e.g., image classification, object detection), and reinforcement learning, often employing transformer networks, neural ordinary differential equations, or recurrent autoencoders. This methodology shows promise in addressing challenges like data scarcity, imbalanced datasets, and computationally expensive training, leading to improved model accuracy and generalization across various applications.