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 efficiency. Larger models tend to achieve higher accuracy but require more resources for training and inference. By distilling the knowledge from a teacher model into a student model, it is possible to create a smaller model that retains a significant portion of the teacher's accuracy while being much faster and requiring less memory. This is particularly useful for deployment on devices with limited computational power, such as mobile phones or embedded systems. The process can involve various techniques, including knowledge distillation, where the student learns from the teacher's probability distributions over classes, or feature distillation, where the student learns to replicate the intermediate feature representations of the teacher.