Transcription of Convolutional Neural Networks
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Convolutional Neural Networks
SimAM: A Simple, Parameter-Free Attention Module forConvolutional Neural NetworksLingxiao Yang1 2 3Ru-Yuan Zhang4 5 Lida Li6 Xiaohua Xie1 2 3 AbstractIn this paper, we propose a conceptually simplebut very effective attention module for convolu - tional Neural Networks (ConvNets). In contrast toexisting channel-wise and spatial-wise attentionmodules, our module instead infers 3-D atten-tion weights for the feature map in a layer with-out adding parameters to the original , we base on some well-known neuro-science theories and propose to optimize an ener-gy function to find the importance of each further derive a fast closed-form solution forthe energy function, and show that the solutioncan be implemented in less than ten lines of advantage of the module is that most ofthe operators are selected based on the solution tothe defined energy function, avoiding too many ef-forts for structure tuning.
Convolutional Neural Networks Lingxiao Yang 1 2 3Ru-Yuan Zhang4 5 Lida Li6 Xiaohua Xie Abstract In this paper, we propose a conceptually simple but very effective attention module for Convolu-tional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial-wise attention modules, our module instead infers 3-D atten-
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