Data augmentation instead of explicit regularization
Modern deep artificial neural networks have achieved impressive results through models with very large capacity---compared to the number of training examples---that control overfitting with the help of different forms of regularization. Regularization can be implicit, as is the case of stochastic gradient descent or parameter sharing in convolutional layers, or explicit. Most common explicit regularization techniques, such as dropout and weight decay, reduce the effective capacity of the model and typically require the use of deeper and wider architectures to compensate for the reduced capacity. Although these techniques have been proven successful in terms of results, they seem to waste capacity. In contrast, data augmentation techniques reduce the generalization error by increasing the number of training examples and without reducing the effective capacity. In this paper we systematically analyze the effect of data augmentation on some popular architectures and conclude that data augmentation alone---without any other explicit regularization techniques---can achieve the same performance or higher as regularized models, especially when training with fewer examples.
 This paper provides a systematic study of data augmentation in image classification problems with deep neural networks and argues that data augmentation could replace some common explicit regularizers like the weight decay and dropout. The data augmentation techniques are also shown to be insensitive to hyper parameters, so easier to use than explicit regularizers when changing architectures. It is good to have a systematic study of data augmentations, however, the materials in this paper in the current state might not be a strong ICLR publication. The paper could potentially be made more interesting or solid if some of the followings could be investigated: - considering a wider range of different problems apart from image classification, and investigate the effectiveness of domain specific data augmentation and general data augmentation - systematically study each of the data augmentation techniques separately to see which is more important (as oppose to only having *light* and *heavy* scheme); potentially also study other less-traditional augmentation schemes such as adversarial examples, etc. - propose novel data augmentation schemes - more analysis of the interplay with Batch Normalization, why the results for BN vs no-BN is not presented for WRN? - carefully designed synthetic (or real) data / task to verify the statements. For example, the explicit regularizers are thought to unnecessarily constraint the model too much. Can you measure the norm (or other complexity measures) of the models learned with explicit regularizers vs models learned with data augmentation?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Data augmentation instead of explicit regularization
Modern deep artificial neural networks have achieved impressive results through models with very large capacity---compared to the number of training examples---that control overfitting with the help of different forms of regularization. Regularization can be implicit, as is the case of stochastic gradient descent or parameter sharing in convolutional layers, or explicit. Most common explicit regularization techniques, such as dropout and weight decay, reduce the effective capacity of the model and typically require the use of deeper and wider architectures to compensate for the reduced capacity. Although these techniques have been proven successful in terms of results, they seem to waste capacity. In contrast, data augmentation techniques reduce the generalization error by increasing the number of training examples and without reducing the effective capacity. In this paper we systematically analyze the effect of data augmentation on some popular architectures and conclude that data augmentation alone---without any other explicit regularization techniques---can achieve the same performance or higher as regularized models, especially when training with fewer examples.
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>



 This paper provides a systematic study of data augmentation in image classification problems with deep neural networks and argues that data augmentation could replace some common explicit regularizers like the weight decay and dropout. The data augmentation techniques are also shown to be insensitive to hyper parameters, so easier to use than explicit regularizers when changing architectures. It is good to have a systematic study of data augmentations, however, the materials in this paper in the current state might not be a strong ICLR publication. The paper could potentially be made more interesting or solid if some of the followings could be investigated: - considering a wider range of different problems apart from image classification, and investigate the effectiveness of domain specific data augmentation and general data augmentation - systematically study each of the data augmentation techniques separately to see which is more important (as oppose to only having *light* and *heavy* scheme); potentially also study other less-traditional augmentation schemes such as adversarial examples, etc. - propose novel data augmentation schemes - more analysis of the interplay with Batch Normalization, why the results for BN vs no-BN is not presented for WRN? - carefully designed synthetic (or real) data / task to verify the statements. For example, the explicit regularizers are thought to unnecessarily constraint the model too much. Can you measure the norm (or other complexity measures) of the models learned with explicit regularizers vs models learned with data augmentation?
assistant
 This paper provides a systematic study of data augmentation in image classification problems with deep neural networks and argues that data augmentation could replace some common explicit regularizers like the weight decay and dropout. The data augmentation techniques are also shown to be insensitive to hyper parameters, so easier to use than explicit regularizers when changing architectures. It is good to have a systematic study of data augmentations, however, the materials in this paper in the current state might not be a strong ICLR publication. The paper could potentially be made more interesting or solid if some of the followings could be investigated: - considering a wider range of different problems apart from image classification, and investigate the effectiveness of domain specific data augmentation and general data augmentation - systematically study each of the data augmentation techniques separately to see which is more important (as oppose to only having *light* and *heavy* scheme); potentially also study other less-traditional augmentation schemes such as adversarial examples, etc. - propose novel data augmentation schemes - more analysis of the interplay with Batch Normalization, why the results for BN vs no-BN is not presented for WRN? - carefully designed synthetic (or real) data / task to verify the statements. For example, the explicit regularizers are thought to unnecessarily constraint the model too much. Can you measure the norm (or other complexity measures) of the models learned with explicit regularizers vs models learned with data augmentation?