selfdistilling
Selfdistilling is a term that has emerged in the context of artificial intelligence, particularly in machine learning and deep learning. It describes a process where a machine learning model learns from itself, often to improve its performance or to create a more compact version of itself. This concept is distinct from traditional self-supervised learning, where a model learns from unlabeled data by creating its own supervisory signals. Selfdistilling typically involves a larger, more complex model (the "teacher") generating "soft targets" or predictions, which are then used to train a smaller, simpler model (the "student"). The student model aims to mimic the behavior and outputs of the teacher model.
The primary goal of selfdistilling is often to achieve a balance between model performance and computational