Convolution
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Tags: knowledge
Convolution
2D convolution using a kernel size of 3, stride of 1 and padding
Dilated Convolutions
(a.k.a. atrous convolutions)
2D convolution using a 3 kernel with a dilation rate of 2 and no padding
Dilation rate: defines a spacing between the values in a kernel.
3x3 kernel with dilation rate = 2 will have the same field of view as a 5x5 kernel, while only using 9 parameters. Imagine taking a 5x5 kernel and deleting every second column and row.
Importance
- Wider field of view at the same computational cost.
- Dilated convolutions are particularly popular in the field of real-time segmentation.
- Useful when need a wide field of view and cannot afford multiple convolutions or larger kernels.
Transposed Convolutions
(a.k.a. ‘deconvolutions’ or fractionally strided convolutions)
- deconvolution - wrong term actually as it refers to the mathematical inverse of what a convolutional layer does
- transposed convolution is similar because it produces the same spatial resolution a hypothetical deconvolutional layer would. However, the actual mathematical operation that’s being performed on the values is different.
- A transposed convolutional layer carries out a regular convolution but reverts its spatial transformation.
Transposed 2D convolution with no padding, stride of 2 and kernel of 3
Importance
- Very helpful for Encoder-Decoder architectures.
- Combine the upscaling of an image with a convolution, instead of doing two separate processes.
Depthwise Separable Convolutions
Importance
- Require much less computations than the standard convolution