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Convolutional neural network: Difference between revisions
→Transpose Convolution
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==Other Types of Convolutions== | ==Other Types of Convolutions== | ||
===Transpose Convolution=== | ===Transpose Convolution=== | ||
See [https://medium.com/apache-mxnet/transposed-convolutions-explained-with-ms-excel-52d13030c7e8 Medium Post]<br> | |||
Instead of your 3x3 kernel taking 9 values as input and returning 1 value (</math>\sum_i \sum_j w_{ij} * i_{i+x,j+y}</math>), the kernel now takes 1 value and returns 9 (</math>w_{ij} * i_{x,y}</math>). | |||
===Gated Convolution=== | ===Gated Convolution=== | ||
See [http://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Free-Form_Image_Inpainting_With_Gated_Convolution_ICCV_2019_paper.html Gated Convolution (ICCV 2019)]<br> | See [http://openaccess.thecvf.com/content_ICCV_2019/html/Yu_Free-Form_Image_Inpainting_With_Gated_Convolution_ICCV_2019_paper.html Gated Convolution (ICCV 2019)]<br> | ||
Given an image, we have two convolution layers <math>k_{feature}</math> and <math>k_{gate}</math>.<br> | Given an image, we have two convolution layers <math>k_{feature}</math> and <math>k_{gate}</math>.<br> | ||
The output is <math>O = \phi(k_{feature}(I)) \odot \sigma(k_{gate}(I))</math> | The output is <math>O = \phi(k_{feature}(I)) \odot \sigma(k_{gate}(I))</math> | ||