  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.
