Attention Based Knowledge Distillation

Attention-based knowledge distillation (KD) aims to compress large, complex neural networks by training smaller "student" models to mimic the behavior of larger "teacher" models, focusing on transferring knowledge from intermediate layers using attention mechanisms. Current research explores various attention strategies, including spatial and frequency domain approaches, and their application across diverse architectures like convolutional neural networks (CNNs) and graph neural networks (GNNs), often incorporating techniques like contrastive learning. This technique is significant for improving efficiency and reducing computational costs in various applications, including image classification, object detection, and speech processing, by enabling the deployment of smaller, faster models without significant performance loss.