Self Distillation

Self-distillation is a machine learning technique where a model learns from its own predictions, improving performance and efficiency without requiring a separate teacher model. Current research focuses on applying self-distillation to diverse tasks and model architectures, including spiking neural networks, transformers, and various deep learning models for image, point cloud, and natural language processing. This approach is particularly valuable for resource-constrained environments, enabling model compression and improved performance in scenarios with limited data or computational power, impacting fields like robotics, medical imaging, and natural language understanding.