The Power of Sparsity in Convolutional Neural Networks
Deep convolutional networks are well-known for their high computational and memory demands. Given limited resources, how does one design a network that balances its size, training time, and prediction accuracy? A surprisingly effective approach to trade accuracy for size and speed is to simply reduce the number of channels in each convolutional layer by a fixed fraction and retrain the network. In many cases this leads to significantly smaller networks with only minimal changes to accuracy. In this paper, we take a step further by empirically examining a strategy for deactivating connections between filters in convolutional layers in a way that allows us to harvest savings both in run-time and memory for many network architectures. More specifically, we generalize 2D convolution to use a channel-wise sparse connection structure and show that this leads to significantly better results than the baseline approach for large networks including VGG and Inception V3.
The paper experiments with channel to channel sparse neural networks. The paper is well written and the analysis is useful. The sparse connection is not new but has not been experimented on large-scale problems like ImageNet. One of the reasons for that is the unavailability of fast implementations of randomly connected convolutional layers. The results displayed in figures 2, 3, 4, and, 5 show that sparse connections need the same number of parameters as the dense networks to reach to the best performance on the given tasks, but can provide better performance when there is a limited budget for the #parameters and #multiplyAdds. This paper is definitely informative but it does not reach to the conference acceptance level, simply because the idea is not new, the sparse connection implementation is poor, and the results are not very surprising.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
The Power of Sparsity in Convolutional Neural Networks
Deep convolutional networks are well-known for their high computational and memory demands. Given limited resources, how does one design a network that balances its size, training time, and prediction accuracy? A surprisingly effective approach to trade accuracy for size and speed is to simply reduce the number of channels in each convolutional layer by a fixed fraction and retrain the network. In many cases this leads to significantly smaller networks with only minimal changes to accuracy. In this paper, we take a step further by empirically examining a strategy for deactivating connections between filters in convolutional layers in a way that allows us to harvest savings both in run-time and memory for many network architectures. More specifically, we generalize 2D convolution to use a channel-wise sparse connection structure and show that this leads to significantly better results than the baseline approach for large networks including VGG and Inception V3.
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 paper experiments with channel to channel sparse neural networks. The paper is well written and the analysis is useful. The sparse connection is not new but has not been experimented on large-scale problems like ImageNet. One of the reasons for that is the unavailability of fast implementations of randomly connected convolutional layers. The results displayed in figures 2, 3, 4, and, 5 show that sparse connections need the same number of parameters as the dense networks to reach to the best performance on the given tasks, but can provide better performance when there is a limited budget for the #parameters and #multiplyAdds. This paper is definitely informative but it does not reach to the conference acceptance level, simply because the idea is not new, the sparse connection implementation is poor, and the results are not very surprising.
assistant
The paper experiments with channel to channel sparse neural networks. The paper is well written and the analysis is useful. The sparse connection is not new but has not been experimented on large-scale problems like ImageNet. One of the reasons for that is the unavailability of fast implementations of randomly connected convolutional layers. The results displayed in figures 2, 3, 4, and, 5 show that sparse connections need the same number of parameters as the dense networks to reach to the best performance on the given tasks, but can provide better performance when there is a limited budget for the #parameters and #multiplyAdds. This paper is definitely informative but it does not reach to the conference acceptance level, simply because the idea is not new, the sparse connection implementation is poor, and the results are not very surprising.