Multi Teacher Distillation

Multi-teacher distillation improves student model performance by leveraging knowledge from multiple, often specialized, teacher models. Current research focuses on optimizing the distillation process through techniques like dynamic teacher selection, distribution balancing (e.g., using Hadamard matrices for standardization), and adaptive learning strategies to address issues such as teacher disagreement and imbalanced data distributions. This approach is proving valuable across diverse applications, including image classification, object detection, pose estimation, and natural language processing, leading to more efficient and accurate models for various tasks. The resulting improvements in model efficiency and accuracy have significant implications for resource-constrained applications and the development of more robust and generalizable AI systems.