AlexEMG / conv_arithmetic

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

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:

• [1] Vincent Dumoulin, Francesco Visin - A guide to convolution arithmetic for deep learning (BibTeX)

Convolution animations

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

No padding, no strides	Arbitrary padding, no strides	Half padding, no strides	Full padding, no strides
No padding, strides	Padding, strides	Padding, strides (odd)

Transposed convolution animations

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

No padding, no strides, transposed	Arbitrary padding, no strides, transposed	Half padding, no strides, transposed	Full padding, no strides, transposed
No padding, strides, transposed	Padding, strides, transposed	Padding, 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