leod

This is a fair point and also called out in the discussion section. To some degree, this could be mitigated by hotloading shader code (and compiling shaders in debug mode). However, this remains as a fundamental downside of the approach.

Personally, I think that this is a price worth paying!

Awesome work.

Would you be willing to share details about the fine-tuning procedure, such as the initialization, learning rate schedule, batch size, etc.? I'd love to learn more.

Background: I've been playing around with generating image sequences from sliding windows of audio. The idea roughly works, but the model training gets stuck due to the difficulty of the task.

Interesting. They train an image classifier to detect images that were generated by a GAN-trained CNN. I wonder if it could be possible to include this classifier in the training loss, such that the generated images fly under its radar as much as possible. If this makes sense, then I guess the cat-and-mouse game just gained another level. On the other hand, what the classifier is detecting could be a fingerprint of the CNN architecture itself.

(Full disclosure: I have only read the abstract so far.)

Thanks! I actually planned to make results shareable at the start, but, knowing the internet, I did not like the idea of being held responsible for whatever content (say offensive or even illegal things) people would put into the titles.