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Sure thing! https://github.com/google/compare_gan/issues/54
It’s not much of a writeup. It’s basically saying, hey, this is zero when it should be one.
The results were dramatic. It went from blobs to replicating the biggan paper almost perfectly. I think we’re at a FID of 11 or so on imagenet.
Stole a year of my life to track it down. But it was a puzzle I couldn’t put down. It haunted my dreams. I was tossing and turning like, but why won’t it work... why won’t it work...
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If I may drop in with a bit of shameless self-promotion.
My "Deep Learning for Programmers: A Tutorial with CUDA, OpenCL, DNNL, Java, and Clojure" book explains and executes every single line of code interactively, from low level operations to high-level networks that do everything automatically. The code is built on the state of the art performance operations of oneDNN (Intel, CPU) and cuDNN (CUDA, GPU). Very concise readable and understandable by humans.
https://aiprobook.com/deep-learning-for-programmers/
Here's the open source library built throughout the book:
https://github.com/uncomplicate/deep-diamond
Some chapters from the beginning of the book are available on my blog, as a tutorial series:
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shameless ad: try mmaction2, where every result is reproducible https://github.com/open-mmlab/mmaction2 . Modelzoo: https://mmaction2.readthedocs.io/en/latest/modelzoo.html
Related posts
- LLaMA-rs: Run inference of LLaMA on CPU with Rust 🦀🦙
- Interactive Programming for Artificial Intelligence Book Series
- Neanderthal, Deep Diamond, and ClojureCUDA now support the latest CUDA 11.7 GPU computing platform.
- Uncomplicate releases with better CUDA compatibility (Deep Diamond, Neanderthal, ClojureCUDA)
- Deep Diamond 0.22.0 released