Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
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 recognition on UCF 101 dataset. Experimental results show that the proposed method achieves better detection, tracking and activity recognition 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.
 The paper presents a synthetic naive approach to analyzing distorted, especially noisy, images through deep neural networks. It uses an existing gating network to discriminate between clean and noisy images, averaging and denoising the latter, so as to somewhat improve the results obtained if no such separation was used. It deals with a well known problem using the deep neural network formulation. Results should be compared to other image analysis methodologies, avoiding smoothing when not required, that can be used for the same purpose. This should also be reflected in related work in section 2; the reason of including Table 1 in it seems unclear.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
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 recognition on UCF 101 dataset. Experimental results show that the proposed method achieves better detection, tracking and activity recognition 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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 The paper presents a synthetic naive approach to analyzing distorted, especially noisy, images through deep neural networks. It uses an existing gating network to discriminate between clean and noisy images, averaging and denoising the latter, so as to somewhat improve the results obtained if no such separation was used. It deals with a well known problem using the deep neural network formulation. Results should be compared to other image analysis methodologies, avoiding smoothing when not required, that can be used for the same purpose. This should also be reflected in related work in section 2; the reason of including Table 1 in it seems unclear.
assistant
 The paper presents a synthetic naive approach to analyzing distorted, especially noisy, images through deep neural networks. It uses an existing gating network to discriminate between clean and noisy images, averaging and denoising the latter, so as to somewhat improve the results obtained if no such separation was used. It deals with a well known problem using the deep neural network formulation. Results should be compared to other image analysis methodologies, avoiding smoothing when not required, that can be used for the same purpose. This should also be reflected in related work in section 2; the reason of including Table 1 in it seems unclear.