Scale Distillation

Scale distillation is a knowledge transfer technique in machine learning that improves the performance of student models by leveraging the knowledge of teacher models trained on simpler, smaller-scale tasks. Current research focuses on applying this approach to various domains, including image super-resolution (using diffusion models like Stable Diffusion) and semi-supervised object detection, addressing challenges like class imbalance and noisy pseudo-labels. This technique enhances the efficiency and effectiveness of training complex models, particularly beneficial when labeled data is scarce or computational resources are limited, leading to improved performance in downstream tasks.