Enhancing Multi-View Stereo With Contrastive Matching and Weighted Focal Loss

Distributed deep learning (DL) plays a critical role in many wireless internet of Things (IoT) applications including remote camera deployment. This work addresses three practical challenges in cyber-deployment of distributed DL over band-limited channels. Specifically, many IoT systems consist of sensor nodes for raw data collection and encoding, and servers for learning and inference tasks. Adaptation of DL over band-limited network data links has only been scantly addressed. A second challenge is the need for pre-deployed encoders being compatible with flexible decoders that can be upgraded or retrained for various learning objectives. The third challenge is the robustness against erroneous training labels. Addressing these three challenges, we develop a hierarchical learning strategy to improve image classification accuracy over band-limited links between sensor nodes and servers. Experimental results show that our hierarchically-trained models can improve link spectrum efficiency without performance loss, reduce storage and computational complexity, and achieve robustness against training label corruption.