TIE-KD: Teacher-Independent and Explainable Knowledge Distillation for Monocular Depth Estimation

arXiv:2402.14340v1 Announce Type: new
Abstract: 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 …

abstract applications arxiv computational cs.cv deep learning distillation framework independent knowledge mde novel transfer type