Pruning Filters for Efficient ConvNets
The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks.
This paper prunes entire groups of filters in CNN so that they reduce computational cost and at the same time do not result in sparse connectivity. This result is important to speed up and compress neural networks while being able to use standard fully-connected linear algebra routines. The results are a 10% improvements in ResNet-like and ImageNet, which may be also achieved with better design of networks. New networks should have been also compared, but this we know it is time-consuming. A good paper with some useful results.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Pruning Filters for Efficient ConvNets
The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks.
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 prunes entire groups of filters in CNN so that they reduce computational cost and at the same time do not result in sparse connectivity. This result is important to speed up and compress neural networks while being able to use standard fully-connected linear algebra routines. The results are a 10% improvements in ResNet-like and ImageNet, which may be also achieved with better design of networks. New networks should have been also compared, but this we know it is time-consuming. A good paper with some useful results.
assistant
This paper prunes entire groups of filters in CNN so that they reduce computational cost and at the same time do not result in sparse connectivity. This result is important to speed up and compress neural networks while being able to use standard fully-connected linear algebra routines. The results are a 10% improvements in ResNet-like and ImageNet, which may be also achieved with better design of networks. New networks should have been also compared, but this we know it is time-consuming. A good paper with some useful results.