Data Augmentation in Training CNNs: Injecting Noise to Images
Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison. The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection. The new approaches will provide better understanding on optimal learning procedures for image classification.
This paper aims at analyzing the effect of injecting noise to images as data augmentation in training CNN for the image classification task. Based on the SSIM metric (which is shown to be a better metric than PSNR), different noise level on a set of different kinds of noise are explored. Experimental results on two sub-datasets of ImageNet suggest that Speckle noise would lead to better CNN models. Even though the simulations appear seemingly convincing, and the conclusion is somewhat interesting to me: speckle noise is recommended which contradicts the general usage of Gaussian noise. The results is too specific to both the model chosen resnet18v2 and also in the chosen dataset. Besides, my bigger concern is that the contribution of this work is highly limited, since there are a bunch of data augmentation techniques: cropping, flipping, color space transformation, rotation, noise injection, etc. Given this broad selection of data augmentation, as far as I know, noise injection is not the most effective nor popular one. In fact, random cropping is the mostly used one that established past a few benchmark CNN models in the imageNet classification task, e.g, ResNet, DenseNet, etc. As such, it would be more convincing if it can be shown that proper noise injection can boost the recognition performance on the ImageNet task. Minors: Abstract line 8, and also introduces -> and also introduce
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Data Augmentation in Training CNNs: Injecting Noise to Images
Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison. The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection. The new approaches will provide better understanding on optimal learning procedures for image classification.
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<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>



This paper aims at analyzing the effect of injecting noise to images as data augmentation in training CNN for the image classification task. Based on the SSIM metric (which is shown to be a better metric than PSNR), different noise level on a set of different kinds of noise are explored. Experimental results on two sub-datasets of ImageNet suggest that Speckle noise would lead to better CNN models. Even though the simulations appear seemingly convincing, and the conclusion is somewhat interesting to me: speckle noise is recommended which contradicts the general usage of Gaussian noise. The results is too specific to both the model chosen resnet18v2 and also in the chosen dataset. Besides, my bigger concern is that the contribution of this work is highly limited, since there are a bunch of data augmentation techniques: cropping, flipping, color space transformation, rotation, noise injection, etc. Given this broad selection of data augmentation, as far as I know, noise injection is not the most effective nor popular one. In fact, random cropping is the mostly used one that established past a few benchmark CNN models in the imageNet classification task, e.g, ResNet, DenseNet, etc. As such, it would be more convincing if it can be shown that proper noise injection can boost the recognition performance on the ImageNet task. Minors: Abstract line 8, and also introduces -> and also introduce
assistant
This paper aims at analyzing the effect of injecting noise to images as data augmentation in training CNN for the image classification task. Based on the SSIM metric (which is shown to be a better metric than PSNR), different noise level on a set of different kinds of noise are explored. Experimental results on two sub-datasets of ImageNet suggest that Speckle noise would lead to better CNN models. Even though the simulations appear seemingly convincing, and the conclusion is somewhat interesting to me: speckle noise is recommended which contradicts the general usage of Gaussian noise. The results is too specific to both the model chosen resnet18v2 and also in the chosen dataset. Besides, my bigger concern is that the contribution of this work is highly limited, since there are a bunch of data augmentation techniques: cropping, flipping, color space transformation, rotation, noise injection, etc. Given this broad selection of data augmentation, as far as I know, noise injection is not the most effective nor popular one. In fact, random cropping is the mostly used one that established past a few benchmark CNN models in the imageNet classification task, e.g, ResNet, DenseNet, etc. As such, it would be more convincing if it can be shown that proper noise injection can boost the recognition performance on the ImageNet task. Minors: Abstract line 8, and also introduces -> and also introduce