Dynamic Channel Pruning: Feature Boosting and Suppression
Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels. FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs. We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification. Experiments show that FBS can respectively provide 5× and 2× savings in compute on VGG-16 and ResNet-18, both with less than 0.6% top-5 accuracy loss.
 The authors propose a dynamic inference technique for accelerating neural network prediction with minimal accuracy loss. The technique prunes channels in an input-dependent way through the addition of auxiliary channel saliency prediction+pruning connections. Pros: - The paper is well-written and clearly explains the technique, and Figure 1 nicely summarizes the weakness of static channel pruning - The technique itself is simple and memory-efficient - The performance decrease is small Cons: - There is no clear motivation for the setting (keeping model accuracy while increasing inference speed by 2x or 5x) - In contrast to methods that prune weights, the model size is not reduced, decreasing the utility in many settings where faster inference and smaller models are desired (e.g. mobile, real-time) - The experiments are limited to classification and fairly dated architectures (VGG16, ResNet-18) Overall, the method is nicely explained but the motivation is not clear. Provided that speeding up inference without reducing the size of the model is desirable, this paper gives a good technique for preserving accuracy.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Dynamic Channel Pruning: Feature Boosting and Suppression
Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources. In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time. FBS introduces small auxiliary connections to existing convolutional layers. In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels. FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs. We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification. Experiments show that FBS can respectively provide 5× and 2× savings in compute on VGG-16 and ResNet-18, both with less than 0.6% top-5 accuracy loss.
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 authors propose a dynamic inference technique for accelerating neural network prediction with minimal accuracy loss. The technique prunes channels in an input-dependent way through the addition of auxiliary channel saliency prediction+pruning connections. Pros: - The paper is well-written and clearly explains the technique, and Figure 1 nicely summarizes the weakness of static channel pruning - The technique itself is simple and memory-efficient - The performance decrease is small Cons: - There is no clear motivation for the setting (keeping model accuracy while increasing inference speed by 2x or 5x) - In contrast to methods that prune weights, the model size is not reduced, decreasing the utility in many settings where faster inference and smaller models are desired (e.g. mobile, real-time) - The experiments are limited to classification and fairly dated architectures (VGG16, ResNet-18) Overall, the method is nicely explained but the motivation is not clear. Provided that speeding up inference without reducing the size of the model is desirable, this paper gives a good technique for preserving accuracy.
assistant
 The authors propose a dynamic inference technique for accelerating neural network prediction with minimal accuracy loss. The technique prunes channels in an input-dependent way through the addition of auxiliary channel saliency prediction+pruning connections. Pros: - The paper is well-written and clearly explains the technique, and Figure 1 nicely summarizes the weakness of static channel pruning - The technique itself is simple and memory-efficient - The performance decrease is small Cons: - There is no clear motivation for the setting (keeping model accuracy while increasing inference speed by 2x or 5x) - In contrast to methods that prune weights, the model size is not reduced, decreasing the utility in many settings where faster inference and smaller models are desired (e.g. mobile, real-time) - The experiments are limited to classification and fairly dated architectures (VGG16, ResNet-18) Overall, the method is nicely explained but the motivation is not clear. Provided that speeding up inference without reducing the size of the model is desirable, this paper gives a good technique for preserving accuracy.