Data Free Knowledge Distillation

Data-free Knowledge Distillation is a technique that transfers the knowledge from a large pre-trained model to a smaller model without using the original training data. Its goal is to enable the smaller model to learn the complex feature representations and decision boundaries of the larger model by generating synthetic samples, thereby maintaining high performance while reducing computational resources and storage requirements. This technology has significant application value in the field of computer vision, especially in scenarios where data privacy is sensitive and data acquisition is difficult.