Recent years have seen the explosion of edge intelligence with powerful Deep
Neural Networks (DNNs). One popular scheme is training DNNs on powerful cloud
servers and subsequently porting them to mobile devices after being
lightweight. Conventional approaches manually specialized DNNs for various edge
platforms and retrain them with real-world data. However, as the number of
platforms increases, these approaches become labour-intensive and
computationally prohibitive. Additionally, real-world data tends to be
sparse-label, further increasing the difficulty of lightweight models. In this
paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices.
Specifically, we simultaneously optimise a large network family using both
labelled and unlabelled data and then automatically search for tailored
networks for different hardware platforms. MatchNAS acts as an intermediary
that bridges the gap between cloud-based DNNs and edge-based DNNs.

在本文中，我们提出了 MatchNAS，一种将深度神经网络移植到移动设备的新方案，通过同时优化一个大型网络集合，使用有标签和无标签数据，并自动搜索适用于不同硬件平台的定制网络，从而作为连接基于云的 DNN 和边缘的 DNN 之间的中间人。

MatchNAS: 通过自动化深度神经网络移植优化稀疏标签数据环境下的边缘人工智能

MatchNAS: Optimizing Edge AI in Sparse-Label Data Contexts via  Automating Deep Neural Network Porting for Mobile Deployment

Porting ML models trained on lab data to real-world situations has long been
a challenge. This paper discusses porting a lab-trained lifting identification
model to the real-world. With performance much lower than on training data, we
explored causes of the failure and proposed four potential solutions to
increase model performance

将在实验室数据上训练的机器学习模型转移到实际情境一直以来都是一个挑战。本文讨论将实验室训练的举重识别模型转移到实际情境中。我们发现其性能远低于训练数据，并提出了四个潜在的解决方案来提高模型性能。