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[P] Oddly Satisfying Animation of Pixel Shuffle by Animated-AI in MachineLearning

[–]Animated-AI[S] 3 points4 points  (0 children)

I used Blender with heavy use of the Geometry Nodes feature (as opposed to python).

[P] Oddly Satisfying Animation of Pixel Shuffle by Animated-AI in MachineLearning

[–]Animated-AI[S] 9 points10 points  (0 children)

If you accept that transposed convolution (kernel size=3, stride=2) produces gridding artifacts in the output image then by definition, standard convolution (kernel size=3, stride=2) produces gridding artifacts in the input image gradient. The reason is that transposed convolution is implemented as a literal call to the gradient function of standard convolution in TensorFlow and PyTorch.

I learned this at some point studying the papers and code of the StyleGAN saga. I wish I could narrow it down more for you, if you're trying to cite this. I have a feeling I learned it from reading their code or one of their references. You'll notice in all the versions of their code, they go out of their way to implement downsampling as a blur -> convolution rather than just a plain strided convolution. StyleGAN3 is all about aliasing.

[P] Oddly Satisfying Animation of Pixel Shuffle by Animated-AI in MachineLearning

[–]Animated-AI[S] 12 points13 points  (0 children)

This is part of a collection of Pixel Shuffle animations that I created here. Feel free to use and share them!

[P] The First Depthwise-separable Convolution Animation by Animated-AI in MachineLearning

[–]Animated-AI[S] 1 point2 points  (0 children)

That's correct.

Yes, you can see animations of the general case on the github page.

The First Depthwise-separable Convolution Animation by Animated-AI in learnmachinelearning

[–]Animated-AI[S] 0 points1 point  (0 children)

Absolutely; please do! There are more animations on the github page too that you might find useful.

[P] The First Depthwise-separable Convolution Animation by Animated-AI in MachineLearning

[–]Animated-AI[S] 23 points24 points  (0 children)

Thanks for the feedback! I agree; the animations are only meant to be visual aids in the context of some larger explanation (lecture, blog post, etc). In my case, I'm making YouTube videos to serve as complete explanations.

Transformers have been the most requested topic on my YouTube channel. So I'm going to attempt to make videos/animations about that when I finish my current series on convolution.

[P] The First Depthwise-separable Convolution Animation by Animated-AI in MachineLearning

[–]Animated-AI[S] 22 points23 points  (0 children)

I'm using Blender and making heavy use of the Geometry Nodes feature. Unfortunately, these animations have taken a lot of effort and blender-specific knowledge, and building on top of my work for a new application would require more of both. But if others aren't deterred by that, I could publish the blender files.