Finally, Convolutions You Can Actually Watch

What it does This repository holds the LaTeX source and animation-generation code for A guide to convolution arithmetic for deep learning, a technical report by Vincent Dumoulin and Francesco Visin. It produces looping GIFs that visualize standard convolutions, transposed convolutions, and dilated convolutions under every common combination of padding and stride. Blue maps are inputs, cyan maps are outputs, and the motion makes the tensor arithmetic explicit.

The interesting bit The project treats convolution less like linear algebra and more like a flipbook. Instead of static diagrams, it renders the kernel sliding, the padding expanding, and the transposed convolution overlapping so you can see why an output tensor ends up exactly the size it does. For a concept that every CNN framework hides behind a single function call, the explicit frame-by-frame motion is oddly clarifying.

Key highlights

• Visualizes standard, transposed, and dilated convolutions with varying padding and stride configurations
• Color-coded maps make inputs and outputs immediately distinguishable
• Exports synchronized GIF, PDF, and PNG frames from the same underlying scenes
• Companion repository to the arXiv technical report A guide to convolution arithmetic for deep learning
• Covers edge cases such as odd-sized padding and stride combinations

Caveats

• The repository is essentially a build system for the paper’s visuals, not an installable library or training framework
• The README stays focused on build and generation instructions; the mathematical exposition lives in the linked paper

Verdict Worth bookmarking if you still pause to calculate output tensor sizes by hand, or if you teach CNNs and need visuals that are clearer than a static diagram. Skip it if you are looking for a drop-in convolution implementation or training utilities.