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 preprocessing 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.
Summary
This paper introduced a parameterized image processing technique to improve a robustness of visual recognition systems against noisy input data. The proposed method is composed of two components; a denoising network that suppresses the noise signals in an image, and gating network that predicts whether to use the original input image or the one produced by the denoising network. The proposed idea is evaluated on three tasks of object detection, tracking and action recognition. 
Originality and significance:
The originality of the paper is very limited since the paper simply combines the existing image denoising technique with the idea of gating. The practical significance of the work is also limited since the model is trained and evaluated with only synthetically generated noise patterns; it is not surprising that the proposed method (both denoising and gating networks) works under this setting, as the noise is created synthetically under the same setting in both training and testing. To demonstrate the practical usefulness, it would be great if the model is evaluated with the actual source of noises (e.g. noises from input sensors, distortion by image compression, etc).  
Clarity:
I think the title of the paper is misleading; the proposed model is actually not a mixture of preprocessing units, as it combines *a* denoising unit together with identity mapping. The gating network is also not designed to incorporate a mixture of more than two preprocessing units, as it outputs only “on/off switches” instead of weights for K mixture components (K>2).
Minor comments:
1) the paper argued the importance of lightweight preprocessing but have not provided analysis on computation costs. From the current results, I don’t see the clear benefit of the proposed method (denoising network) over the average filtering considering the tradeoff between computation vs. performance. 
2) In Figure 5, I suggest highlighting the differences among the examples for clarity.