Differentiable JPEG-based Input Perturbation for Knowledge Distillation Amplification via Conditional Mutual Information Maximization

Maximizing conditional mutual information (CMI) has recently been shown to enhance the effectiveness of teacher networks in knowledge distillation (KD). Prior work achieves this by fine-tuning a pretrained teacher to maximize a proxy of its CMI. However, fine-tuning large-scale teachers is often impractical, and proxy-based optimization introduces inaccuracies. To overcome these limitations, we propose Differentiable JPEG-based Input Perturbation (DJIP), a plug-and-play framework that improves teacher–student knowledge transfer without modifying the teacher. DJIP employs a trainable differentiable JPEG layer inserted before the teacher to perturb teacher inputs in a way that directly increases CMI. We further introduce a novel alternating optimization algorithm to efficiently learn the coding parameters of the JPEG layer to maximize the perturbed CMI. Extensive experiments on CIFAR-100 and ImageNet, across diverse distillers and architectures, demonstrate that DJIP consistently improves student accuracy-achieving up to 4.11% gains-while remaining computationally lightweight and fully compatible with standard KD pipelines.