Filter redistribution templates for iteration-lessconvolutional model reduction
Automatic neural network discovery methods face an enormous challenge caused for the size of the search space. A common practice is to split this space at different levels and to explore only a part of it. Neural architecture search methods look for how to combine a subset of layers, which are the most promising, to create an architecture while keeping a predefined number of filters in each layer. On the other hand, pruning techniques take a well known architecture and look for the appropriate number of filters per layer. In both cases the exploration is made iteratively, training models several times during the search. Inspired by the advantages of the two previous approaches, we proposed a fast option to find models with improved characteristics. We apply a small set of templates, which are considered promising, for make a redistribution of the number of filters in an already existing neural network. When compared to the initial base models, we found that the resulting architectures, trained from scratch, surpass the original accuracy even after been reduced to fit the same amount of resources.
This paper investigates the impact of several predefined filter templates, including uniform template, reverse template, quadratic template, and negative quadratic template, to the performance of different neural networks such as VGG-19, ResNet-50, Inception Network, and MobileNet. This paper uses some templates. However, it is impossible to enumerate all possible templates and hence some good templates may not be included and studied in this paper, which makes the empirical studies in this paper less useful. In experiments, authors need to compare with the results of neural architecture search. Based on such comparison, we can see whether the templates used in this paper are reasonable. In experiments, it seems that different templates may have their own characteristics and their usefulness also depends on the neural network used. So it is not easy to make general conclusion.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Filter redistribution templates for iteration-lessconvolutional model reduction
Automatic neural network discovery methods face an enormous challenge caused for the size of the search space. A common practice is to split this space at different levels and to explore only a part of it. Neural architecture search methods look for how to combine a subset of layers, which are the most promising, to create an architecture while keeping a predefined number of filters in each layer. On the other hand, pruning techniques take a well known architecture and look for the appropriate number of filters per layer. In both cases the exploration is made iteratively, training models several times during the search. Inspired by the advantages of the two previous approaches, we proposed a fast option to find models with improved characteristics. We apply a small set of templates, which are considered promising, for make a redistribution of the number of filters in an already existing neural network. When compared to the initial base models, we found that the resulting architectures, trained from scratch, surpass the original accuracy even after been reduced to fit the same amount of resources.
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 investigates the impact of several predefined filter templates, including uniform template, reverse template, quadratic template, and negative quadratic template, to the performance of different neural networks such as VGG-19, ResNet-50, Inception Network, and MobileNet. This paper uses some templates. However, it is impossible to enumerate all possible templates and hence some good templates may not be included and studied in this paper, which makes the empirical studies in this paper less useful. In experiments, authors need to compare with the results of neural architecture search. Based on such comparison, we can see whether the templates used in this paper are reasonable. In experiments, it seems that different templates may have their own characteristics and their usefulness also depends on the neural network used. So it is not easy to make general conclusion.
assistant
This paper investigates the impact of several predefined filter templates, including uniform template, reverse template, quadratic template, and negative quadratic template, to the performance of different neural networks such as VGG-19, ResNet-50, Inception Network, and MobileNet. This paper uses some templates. However, it is impossible to enumerate all possible templates and hence some good templates may not be included and studied in this paper, which makes the empirical studies in this paper less useful. In experiments, authors need to compare with the results of neural architecture search. Based on such comparison, we can see whether the templates used in this paper are reasonable. In experiments, it seems that different templates may have their own characteristics and their usefulness also depends on the neural network used. So it is not easy to make general conclusion.