Curriculum Distillation

Curriculum distillation is a machine learning technique that improves the efficiency and effectiveness of knowledge transfer from a complex "teacher" model to a simpler "student" model by structuring the training process. Current research focuses on developing sophisticated curriculum strategies, such as incorporating uncertainty measures or leveraging generative models to guide the learning process, particularly for large-scale datasets and challenging tasks like image segmentation and multilingual question answering. These advancements enhance model performance, robustness, and scalability, leading to more efficient and effective deployment of deep learning models across various applications.