Abstract
Deep learning on an edge device requires energy efficient operation due to ever diminishing power budget. Intentional low quality data during the data acquisition for longer battery life, and natural noise from the low cost sensor degrade the quality of target output which hinders adoption of deep learning on an edge device. To overcome these problems, we propose simple yet efficient mixture of pre-processing experts (MoPE) model to handle various image distortions including low resolution and noisy images. We also propose to use adversarially trained auto encoder as a pre-processing expert for the noisy images. We evaluate our proposed method for various machine learning tasks including object detection on MS-COCO 2014 dataset, multiple object tracking problem on MOT-Challenge dataset, and human activity classification on UCF 101 dataset. Experimental results show that the proposed method achieves better detection, tracking and activity classification accuracies under noise without sacrificing accuracies for the clean images. The overheads of our proposed MoPE are 0.67% and 0.17% in terms of memory and computation compared to the baseline object detection network.
Abstract (translated)
由于功率预算不断减少，对边缘设备的深入学习需要节能操作。为了延长电池寿命，数据采集过程中故意的低质量数据以及低成本传感器的自然噪声会降低目标输出的质量，这阻碍了边缘设备的深度学习。为了克服这些问题，我们提出了一种简单而有效的预处理专家（MOPE）模型来处理各种图像失真，包括低分辨率和噪声图像。我们还建议使用对手训练的自动编码器作为预处理专家的噪声图像。我们评估了我们提出的各种机器学习任务的方法，包括MS-COCO 2014数据集上的目标检测、MOT挑战数据集上的多目标跟踪问题以及UCF 101数据集上的人类活动分类。实验结果表明，该方法在不牺牲图像精度的前提下，在噪声环境下实现了较好的检测、跟踪和活动分类精度。与基线目标检测网络相比，我们提出的MOPE在内存和计算方面的开销分别为0.67%和0.17%。
URL
https://arxiv.org/abs/1904.12426
