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A technical report on convolution arithmetic in the context of deep learning

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Convolution arithmetic

A technical report on convolution arithmetic in the context of deep learning.

The code and/or the images of this tutorial are free to use for non-commercial purposes with proper attribution:

Convolution animations

N.B.: Blue maps are inputs, and cyan maps are outputs.

No padding, no stridesArbitrary padding, no stridesHalf padding, no stridesFull padding, no strides
No padding, stridesPadding, stridesPadding, strides (odd)

Transposed convolution animations

N.B.: Blue maps are inputs, and cyan maps are outputs.

No padding, no strides, transposedArbitrary padding, no strides, transposedHalf padding, no strides, transposedFull padding, no strides, transposed
No padding, strides, transposedPadding, strides, transposedPadding, strides, transposed (odd)

Dilated convolution animations

N.B.: Blue maps are inputs, and cyan maps are outputs.

No padding, no stride, dilation

Generating the Makefile

From the repository's root directory:

$ ./bin/generate_makefile

Generating the animations

From the repository's root directory:

$ make all_animations

The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory.

Compiling the document

From the repository's root directory:

$ make

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A technical report on convolution arithmetic in the context of deep learning

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