Semantic segmentation plays a critical role in applications like predictive maintenance and quality inspection but remains challenging to deploy on resource-constrained devices due to their computational demands. This paper introduces Explainable AI (XAI)-Guided Knowledge Distillation (XGD), a method that combines pixel-wise class probability alignment with saliency map refinement guided by XAI. XGD focuses on refining features at the first decoder layer, allowing lightweight student networks to replicate the performance of more complex teacher models. Experiments conducted on the TTPLA, Substation, and Pascal VOC 2012 datasets show that XGD consistently improves segmentation performance, achieving up to a 4.57% increase in mIoU for the DeepLabV3+ student network with ResNet101 backbone. Ablation studies demonstrate the effects of XGD’s distillation losses, while hyperparameter analysis identifies the optimal settings for efficient knowledge transfer. XGD surpasses existing distillation approaches across various models and backbones, providing an effective, resource-efficient, high-performance approach.
Nguyen et al. (Mon,) studied this question.