Knowledge Distillation Using Deep Learning Techniques: A Survey

In Deep Neural Networks (DNNs), optimization is necessary for adjusting model parameters to reduce the loss function, which directly affects the model’s performance. Effective optimization techniques reduce prediction errors and help models generalize new data better. One method discussed in this document is Knowledge Distillation (KD), where a “student” model which is smaller is trained using the knowledge imparted by a larger, more complex “teacher” model. KD was first focused on DNN compression for resource-efficient deep learning, but it has now been extended to privileged learning, adversarial defense, and noisy data learning. Some of the most important techniques in distillation include online distillation, involving training Teacher and student models operate together. for end-to-end optimization; offline distillation, where the teacher model guides the student model based on lessons learned; and self-distillation where a model learns from its previous iterations. Advanced methods such as adversarial distillation, which combines adversarial procedures to improve information transmission, and lifetime distillation, in which historical data educates current models to preserve stability and avoid performance decline. Applications span across domains like Computer vision (CV), Natural Language Processing (NLP) and speech recognition, demonstrating the versatility and importance of KD in deploying powerful models in resource-constrained environments.