Although deep learning has achieved remarkable successes over the past years, few reports have been published about applying deep neural networks to Wireless Sensor Networks (WSNs) for image targets recognition where data, energy, computation resources are limited. In this work, a Cost-Effective Domain Generalization (CEDG) algorithm has been proposed to train an efficient network with minimum labor requirements. CEDG transfers networks from a publicly available source domain to an application-specific target domain through an automatically allocated synthetic domain. The target domain is isolated from parameters tuning and used for model selection and testing only. The target domain is significantly different from the source domain because it has new target categories and is consisted of low-quality images that are out of focus, low in resolution, low in illumination, low in photographing angle. The trained network has about 7M (ResNet-20 is about 41M) multiplications per prediction that is small enough to allow a digital signal processor chip to do real-time recognitions in our WSN. The category-level averaged error on the unseen and unbalanced target domain has been decreased by 41.12%.

中文翻译：

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使用松散和弱标记图像为无线传感器网络训练深度神经网络

尽管深度学习在过去几年取得了显着的成功，但很少有关于将深度神经网络应用于无线传感器网络 (WSN) 以在数据、能量和计算资源有限的情况下进行图像目标识别的报告。在这项工作中，提出了一种具有成本效益的域泛化 (CEDG) 算法来训练一个具有最少劳动力需求的高效网络。CEDG 通过自动分配的合成域将网络从公开可用的源域传输到特定于应用程序的目标域。目标域与参数调整隔离，仅用于模型选择和测试。目标域与源域显着不同，因为它具有新的目标类别，并且由失焦、分辨率低的低质量图像组成，照度低，拍摄角度低。经过训练的网络每个预测有大约 7M（ResNet-20 大约是 41M）次乘法，这个乘法小到足以让数字信号处理器芯片在我们的 WSN 中进行实时识别。看不见的和不平衡的目标域的类别级平均误差降低了 41.12%。