  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.
