Convolutional neural network: Difference between revisions
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===Groups=== | ===Groups=== | ||
==Types of Convolutions== | ==Other Types of Convolutions== | ||
===Transpose Convolution==== | ===Transpose 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> | |||
Given an image, we have two convolution layers <math>k_{feature}</math> and <math>k_{gate}</math>. | |||
The output is <math>O = \phi(k_{feature}(I)) \odot \sigma(k_{gate}(I))</math> | |||
Revision as of 17:28, 24 February 2020
Convolutional Neural Network
Primarily used for image tasks such as computer vision or image generation,
though they can be used anywhere you have a rectangular grid with spatial relationship among your data.
Convolutions
Pytorch Convolution Layers
Here, we will explain 2d convolutions.
Suppose we have the following input image:
and the following 3x3 kernel:
For each possible position of the 3x3 kernel over the input image, we perform an element-wise multiplication (\(\displaystyle \odot\)) and sum over all entries to get a single value.
Stride
Padding
Dilation
Groups
Other Types of Convolutions
Transpose Convolution
Gated Convolution
See Gated Convolution (ICCV 2019)
Given an image, we have two convolution layers \(\displaystyle k_{feature}\) and \(\displaystyle k_{gate}\).
The output is \(\displaystyle O = \phi(k_{feature}(I)) \odot \sigma(k_{gate}(I))\)