Excitation Networks
Found 5 free book(s)1 Squeeze-and-Excitation Networks - arXiv
arxiv.org1 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 …
arXiv:1910.03151v4 [cs.CV] 7 Apr 2020
arxiv.orgOne 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
Coordinate Attention for Efficient Mobile Network Design
openaccess.thecvf.comable 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-
Dynamic Convolution: Attention Over Convolution Kernels
openaccess.thecvf.comwise 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-
PREPRINT 1 Explainability in Graph Neural Networks: A ...
arxiv.orgPREPRINT 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