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[1709.01507] 挤压励磁网络
Source: arxiv.org
Article summary:
1. This article presents a novel building block called “Squeeze-and-Excitation” (SE) module, which adaptively re-calibrates channels by explicitly modeling the interdependencies between channels to reflect responses.
2. The SE blocks can be stacked together to form SENet architectures, which are shown to generalize effectively in different datasets.
3. The SE blocks significantly improve existing state-of-the-art CNNs with only a slight increase in computational cost, and the model achieved first place in the 2017 ImageNet Large Scale Visual Recognition Challenge with a relative improvement of ~25% over the winning entry from 2016.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence for its claims through experiments conducted on different datasets and comparison with existing state-of-the-art CNNs. It also provides a link to the code and model used for further verification of its results. However, there is no discussion of potential risks associated with using this technology or any counterarguments that may exist against it. Additionally, there is no mention of any ethical considerations related to using this technology such as privacy concerns or potential misuse of data collected by these networks. Furthermore, while the article does provide evidence for its claims, it does not explore any alternative explanations or theories that could explain the results obtained from its experiments.