Context-Gated Convolution
As the basic building block of Convolutional Neural Networks (CNNs), the convolutional layer is designed to extract local patterns and lacks the ability to model global context in its nature. Many efforts have been recently made to complement CNNs with the global modeling ability, especially by a family of works on global feature interaction. In these works, the global context information is incorporated into local features before they are fed into convolutional layers. However, research on neuroscience reveals that, besides influences changing the inputs to our neurons, the neurons* ability of modifying their functions dynamically according to context is essential for perceptual tasks, which has been overlooked in most of CNNs. Motivated by this, we propose one novel Context-Gated Convolution (CGC) to explicitly modify the weights of convolutional layers adaptively under the guidance of global context. As such, being aware of the global context, the modulated convolution kernel of our proposed CGC can better extract representative local patterns and compose discriminative features. Moreover, our proposed CGC is lightweight, amenable to modern CNN architectures, and consistently improves the performance of CNNs according to extensive experiments on image classification, action recognition, and machine translation.
This paper proposes a novel and interesting idea of modulating convolutional kernels with global context. They use depthwise factorized channel-wise interaction to reduce complexity and parameters. While an interesting idea, I found it hard to see how the global context would usefully modulate the kernels. While Figure 1 illustrates the idea, it is not clear how the modulated kernel is better suited for the task relative to the original kernel. It would be nice if you could show a simple/toy example where your modulation clearly modifies the kernel in a way that is intuitively useful. Overall, I believe the motivation for the method could be made clearer - Why is this the best way to incorporate global context? It is also unclear to me how the proposed network relates to the motivating and cited Neuroscience literature. It is nice that the computational complexity is carefully tracked. The CGC network does seem to converge faster but the benefits to validation accuracy seem modest in Table 1/Fig 3. It would be interesting to further explore what is improving the convergence speed -i.e. to test your supposition that it *improves the model*s generalization ability and the gating mechanism reduces the norm of gradients back-propagated to he convolution kernels ...*. The experiments are well described and extensive and some of them do give some more substantial performance improvements, but overall I think the paper does not explain the motivation for the algorithm sufficiently for ICLR publication.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Context-Gated Convolution
As the basic building block of Convolutional Neural Networks (CNNs), the convolutional layer is designed to extract local patterns and lacks the ability to model global context in its nature. Many efforts have been recently made to complement CNNs with the global modeling ability, especially by a family of works on global feature interaction. In these works, the global context information is incorporated into local features before they are fed into convolutional layers. However, research on neuroscience reveals that, besides influences changing the inputs to our neurons, the neurons* ability of modifying their functions dynamically according to context is essential for perceptual tasks, which has been overlooked in most of CNNs. Motivated by this, we propose one novel Context-Gated Convolution (CGC) to explicitly modify the weights of convolutional layers adaptively under the guidance of global context. As such, being aware of the global context, the modulated convolution kernel of our proposed CGC can better extract representative local patterns and compose discriminative features. Moreover, our proposed CGC is lightweight, amenable to modern CNN architectures, and consistently improves the performance of CNNs according to extensive experiments on image classification, action recognition, and machine translation.
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 proposes a novel and interesting idea of modulating convolutional kernels with global context. They use depthwise factorized channel-wise interaction to reduce complexity and parameters. While an interesting idea, I found it hard to see how the global context would usefully modulate the kernels. While Figure 1 illustrates the idea, it is not clear how the modulated kernel is better suited for the task relative to the original kernel. It would be nice if you could show a simple/toy example where your modulation clearly modifies the kernel in a way that is intuitively useful. Overall, I believe the motivation for the method could be made clearer - Why is this the best way to incorporate global context? It is also unclear to me how the proposed network relates to the motivating and cited Neuroscience literature. It is nice that the computational complexity is carefully tracked. The CGC network does seem to converge faster but the benefits to validation accuracy seem modest in Table 1/Fig 3. It would be interesting to further explore what is improving the convergence speed -i.e. to test your supposition that it *improves the model*s generalization ability and the gating mechanism reduces the norm of gradients back-propagated to he convolution kernels ...*. The experiments are well described and extensive and some of them do give some more substantial performance improvements, but overall I think the paper does not explain the motivation for the algorithm sufficiently for ICLR publication.
assistant
This paper proposes a novel and interesting idea of modulating convolutional kernels with global context. They use depthwise factorized channel-wise interaction to reduce complexity and parameters. While an interesting idea, I found it hard to see how the global context would usefully modulate the kernels. While Figure 1 illustrates the idea, it is not clear how the modulated kernel is better suited for the task relative to the original kernel. It would be nice if you could show a simple/toy example where your modulation clearly modifies the kernel in a way that is intuitively useful. Overall, I believe the motivation for the method could be made clearer - Why is this the best way to incorporate global context? It is also unclear to me how the proposed network relates to the motivating and cited Neuroscience literature. It is nice that the computational complexity is carefully tracked. The CGC network does seem to converge faster but the benefits to validation accuracy seem modest in Table 1/Fig 3. It would be interesting to further explore what is improving the convergence speed -i.e. to test your supposition that it *improves the model*s generalization ability and the gating mechanism reduces the norm of gradients back-propagated to he convolution kernels ...*. The experiments are well described and extensive and some of them do give some more substantial performance improvements, but overall I think the paper does not explain the motivation for the algorithm sufficiently for ICLR publication.