Monocular depth estimation (MDE) is essential for numerous applications yet is impeded by the substantial computational demands of accurate deep learning models. To mitigate this, we introduce a novel Teacher-Independent Explainable Knowledge Distillation (TIE-KD) framework that streamlines the knowledge transfer from complex teacher models to compact student networks, eliminating the need for architectural similarity. The cornerstone of TIE-KD is the Depth Probability Map (DPM), an explainable feature map that interprets the teacher's output, enabling feature-based knowledge distillation solely from the teacher's response. This approach allows for efficient student learning, leveraging the strengths of feature-based distillation. Extensive evaluation of the KITTI dataset indicates that TIE-KD not only outperforms conventional response-based KD methods but also demonstrates consistent efficacy across diverse teacher and student architectures. The robustness and adaptability of TIE-KD underscore its potential for applications requiring efficient and interpretable models, affirming its practicality for real-world deployment.

一种名为TIE-KD的新型教师无关可解释知识蒸馏框架，基于深度概率图(DPM)对教师模型的输出进行解释，实现了高效的基于特征的知识蒸馏，提高了紧凑型学生模型的学习效能，并在评估了KITTI数据集后表现出了优于常规响应型知识蒸馏方法的稳健性和适应性。这种方法具有较高的潜力，适用于需要高效、可解释的模型的实际应用和部署。

TIE-KD： 基于教师独立和可解释的知识蒸馏的单目深度估计

TIE-KD：基于教师独立和可解释的知识蒸馏的单目深度估计