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Squeeze-and-Excitation Networks. Contribute to hujie-frank/SENet development by creating an account on GitHub.
SENet. Squeeze-and-Excitation Networks. Cuda 2.7k 772 · GENet. C++ 225 31 · Places2-CNNs. Forked from lishen-shirley/Places2-CNNs. Places2-401-CNN and Places2-365 ...
We show that these blocks can be stacked together to form SENet ... Models and code are available at https://github.com/hujie-frank/SENet. read more.
由 J Hu 著作2017被引用 7716 次 — available at https://github.com/hujie-frank/SENet. Index Terms—Squeeze-and-Excitation, Image representations, Attention, Convolutional Neural Networks.
Github 镜像仓库 源项目地址. ... The SENet-154 is one of our superior models used in ILSVRC 2017 Image Classification ... https://github.com/hujie-frank/senet.
由 J Hu 著作2018被引用 7716 次 — Code and models are available at https: //github.com/hujie-frank/SENet. Related Material [pdf] [supp] [arXiv] [video]. [bibtex].
由 J Hu 著作2018被引用 7716 次 — Code and models are available at https://github.com/hujie-frank/SENet. ... Persistent Link: https://xplorestaging.ieee.org/servlet/opac?punumber=8576498
... achieving a ∼25% relative improvement over the winning en- try of 2016. Code and models are available at https: //github.com/hujie-frank/SENet.
由 J Hu 著作2020被引用 7716 次 — Models and code are available at https://github.com/hujie-frank/SENet. Publication types. Research Support, Non-U.S. Gov't.

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