Learning Internal Dense But External Sparse Structures of Deep Neural Network
Recent years have witnessed two seemingly opposite developments of deep convolutional neural networks (CNNs). On one hand, increasing the density of CNNs by adding cross-layer connections achieve higher accuracy. On the other hand, creating sparsity structures through regularization and pruning methods enjoys lower computational costs. In this paper, we bridge these two by proposing a new network structure with locally dense yet externally sparse connections. This new structure uses dense modules, as basic building blocks and then sparsely connects these modules via a novel algorithm during the training process. Experimental results demonstrate that the locally dense yet externally sparse structure could acquire competitive performance on benchmark tasks (CIFAR10, CIFAR100, and ImageNet) while keeping the network structure slim.
 The authors bridge two components (density of CNNs and sparsity structures) by proposing a new network structure with locally dense yet externally sparse connections. + Combination of being dense and sparse is an interesting area. - Although experiment results demonstrate evolving sparse connection could reach competitive results, it would be interesting to show how separating a network into several small networks is useful, for example, interpretablity of deep neural network. There is an interesting work: "Using deep learning to model the hierarchical structure and function of a cell" https://www.nature.com/articles/nmeth.4627
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Internal Dense But External Sparse Structures of Deep Neural Network
Recent years have witnessed two seemingly opposite developments of deep convolutional neural networks (CNNs). On one hand, increasing the density of CNNs by adding cross-layer connections achieve higher accuracy. On the other hand, creating sparsity structures through regularization and pruning methods enjoys lower computational costs. In this paper, we bridge these two by proposing a new network structure with locally dense yet externally sparse connections. This new structure uses dense modules, as basic building blocks and then sparsely connects these modules via a novel algorithm during the training process. Experimental results demonstrate that the locally dense yet externally sparse structure could acquire competitive performance on benchmark tasks (CIFAR10, CIFAR100, and ImageNet) while keeping the network structure slim.
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 bridge two components (density of CNNs and sparsity structures) by proposing a new network structure with locally dense yet externally sparse connections. + Combination of being dense and sparse is an interesting area. - Although experiment results demonstrate evolving sparse connection could reach competitive results, it would be interesting to show how separating a network into several small networks is useful, for example, interpretablity of deep neural network. There is an interesting work: "Using deep learning to model the hierarchical structure and function of a cell" https://www.nature.com/articles/nmeth.4627
assistant
 The authors bridge two components (density of CNNs and sparsity structures) by proposing a new network structure with locally dense yet externally sparse connections. + Combination of being dense and sparse is an interesting area. - Although experiment results demonstrate evolving sparse connection could reach competitive results, it would be interesting to show how separating a network into several small networks is useful, for example, interpretablity of deep neural network. There is an interesting work: "Using deep learning to model the hierarchical structure and function of a cell" https://www.nature.com/articles/nmeth.4627