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.
The search/design space of neural network architectures is vast, so researchers tend to use simple heuristics to guide their designs; alternatively neural architecture search methods may minimise heuristics in order to remove bias within the search. The authors propose applying a few simple heuristics in the form of *templates* for the number of convolutional filters across the different layers within an architecture. Apart from reversing the filter distribution, which starts a network with a large amount of filters and then reduces them (given how the filter number is generally increased with depth and reduction in spatial resolution), the authors also propose using the same number of filters per layer (*uniform*), as well as *quadratic* and *negative quadratic* distributions. There does not seem to be any particular motivation for these patterns, but this is at least better than poor justifications. The results include 4 common CNN architectures and experiments on CIFAR-10 and CIFAR-100, which is acceptable, but only single results are shown, which makes it hard to judge the significance of the performance of the redistributed networks, especially given that there seems to be no particular trend in the performance of any of the templates. The accuracy vs. parameter count results are interesting, as the default architecture does worse a good amount of the time. Unfortunately memory footprint results are mixed, and the default architectures tend to perform much better with respect to inference time. Most of the figures are hard to read and not labelled completely (e.g. relying on the caption to dictate which column corresponds to which architecture), so these should be reworked. Ultimately, the results are largely empirical, and the work would benefit from a better exploration for the consequences of using these templates, and any sort of rule or correlation that links these to successful architectures - at the moment it is only a somewhat interesting observation. Combined with the lack of multiple runs to provide more solid evidence for the empirical findings, I would reject this work.
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>



The search/design space of neural network architectures is vast, so researchers tend to use simple heuristics to guide their designs; alternatively neural architecture search methods may minimise heuristics in order to remove bias within the search. The authors propose applying a few simple heuristics in the form of *templates* for the number of convolutional filters across the different layers within an architecture. Apart from reversing the filter distribution, which starts a network with a large amount of filters and then reduces them (given how the filter number is generally increased with depth and reduction in spatial resolution), the authors also propose using the same number of filters per layer (*uniform*), as well as *quadratic* and *negative quadratic* distributions. There does not seem to be any particular motivation for these patterns, but this is at least better than poor justifications. The results include 4 common CNN architectures and experiments on CIFAR-10 and CIFAR-100, which is acceptable, but only single results are shown, which makes it hard to judge the significance of the performance of the redistributed networks, especially given that there seems to be no particular trend in the performance of any of the templates. The accuracy vs. parameter count results are interesting, as the default architecture does worse a good amount of the time. Unfortunately memory footprint results are mixed, and the default architectures tend to perform much better with respect to inference time. Most of the figures are hard to read and not labelled completely (e.g. relying on the caption to dictate which column corresponds to which architecture), so these should be reworked. Ultimately, the results are largely empirical, and the work would benefit from a better exploration for the consequences of using these templates, and any sort of rule or correlation that links these to successful architectures - at the moment it is only a somewhat interesting observation. Combined with the lack of multiple runs to provide more solid evidence for the empirical findings, I would reject this work.
assistant
The search/design space of neural network architectures is vast, so researchers tend to use simple heuristics to guide their designs; alternatively neural architecture search methods may minimise heuristics in order to remove bias within the search. The authors propose applying a few simple heuristics in the form of *templates* for the number of convolutional filters across the different layers within an architecture. Apart from reversing the filter distribution, which starts a network with a large amount of filters and then reduces them (given how the filter number is generally increased with depth and reduction in spatial resolution), the authors also propose using the same number of filters per layer (*uniform*), as well as *quadratic* and *negative quadratic* distributions. There does not seem to be any particular motivation for these patterns, but this is at least better than poor justifications. The results include 4 common CNN architectures and experiments on CIFAR-10 and CIFAR-100, which is acceptable, but only single results are shown, which makes it hard to judge the significance of the performance of the redistributed networks, especially given that there seems to be no particular trend in the performance of any of the templates. The accuracy vs. parameter count results are interesting, as the default architecture does worse a good amount of the time. Unfortunately memory footprint results are mixed, and the default architectures tend to perform much better with respect to inference time. Most of the figures are hard to read and not labelled completely (e.g. relying on the caption to dictate which column corresponds to which architecture), so these should be reworked. Ultimately, the results are largely empirical, and the work would benefit from a better exploration for the consequences of using these templates, and any sort of rule or correlation that links these to successful architectures - at the moment it is only a somewhat interesting observation. Combined with the lack of multiple runs to provide more solid evidence for the empirical findings, I would reject this work.