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Excitation Networks

Found 5 free book(s)
1 Squeeze-and-Excitation Networks - arXiv

1 Squeeze-and-Excitation Networks - arXiv

arxiv.org

1 Squeeze-and-Excitation Networks Jie Hu [000000025150 1003] Li Shen 2283 4976] Samuel Albanie 0001 9736 5134] Gang Sun [00000001 6913 6799] Enhua Wu 0002 2174 1428] Abstract—The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within …

  Network, Excitation, Squeeze and excitation networks, Squeeze

arXiv:1910.03151v4 [cs.CV] 7 Apr 2020

arXiv:1910.03151v4 [cs.CV] 7 Apr 2020

arxiv.org

One of the representative methods is squeeze-and-excitation networks (SENet) [14], which learns channel attention for each convolution block, bringing clear performance gain for various deep CNN architectures. Following the setting of squeeze (i.e., feature ag-gregation) and excitation (i.e., feature recalibration) in

  Network, Excitation, Excitation networks

Coordinate Attention for Efficient Mobile Network Design

Coordinate Attention for Efficient Mobile Network Design

openaccess.thecvf.com

able for mobile networks. Considering the restricted computation capacity of mo-bile networks, to date, the most popular attention mech-anism for mobile networks is still the Squeeze-and-Excitation (SE) attention [18]. It computes channel atten-tion with the help of 2D global pooling and provides no-

  Network, Excitation

Dynamic Convolution: Attention Over Convolution Kernels

Dynamic Convolution: Attention Over Convolution Kernels

openaccess.thecvf.com

wise convolution, channel shuffle, squeeze-and-excitation [12], asymmetric convolution [5]) and architecture search ([27, 6, 2]) are important for designing efficient convolu-tional neural networks. However, even the state-of-the-art efficient CNNs (e.g. MobileNetV3 [10]) suffer significant performance degrada-

  Network, Excitation

PREPRINT 1 Explainability in Graph Neural Networks: A ...

PREPRINT 1 Explainability in Graph Neural Networks: A ...

arxiv.org

PREPRINT 1 Explainability in Graph Neural Networks: A Taxonomic Survey Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji Abstract—Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks.A major limitation of

  Network

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