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This is important for vision ML because sufficiently advanced simulations may comprise the real world as a special case: https://arxiv.org/abs/1703.06907 https:

This is important for vision ML because sufficiently advanced simulations may comprise the real world as a special case: https://arxiv.org/abs/1703.06907 https://arxiv.org/abs/1703.06907 For example, gaming pixel maps have been used for semantic simulation, and have been rendered scenes: https://arxiv.org/abs/1608.02192 https://arxiv.org/abs/1608.02192 https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Ros_The_SYNTHIA_Dataset_CVPR_2016_paper.pdf https://www.cv-foundation.org/openaccess/content_cvpr_2016/p... This concept has not (yet) been applied in audio ML. We have a paper in submission---will be on ArXiv soon---where we share a GPU-enabled modular synthesizer that is 16000x faster than realtime, concurrently released with a 1-billion audio sample corpus that is 100x larger than any audio dataset in the literature. Here's the code: https://github.com/torchsynth/torchsynth https://github.com/torchsynth/torchsynth

GuB-42 6y ago
How is it related to ML? Closed form implicit surfaces are actually one of the most difficult way to model the real world. They are neat because they are very compact and the creation process is close to modeling with (mathematical) clay. But they are hard to use if you want to model the real world with all its complexity, resulting of a variety of chemical and physical processes happening over time. There is a reason why they are so popular in the demoscene, for which technical achievement and art is more important than realism, and not much elsewhere. The paper is about making rendering of these primitives more efficient, which may prove to be a great addition to an artist toolbox, and maybe for scientific imagery. However, I don't really see applications for ML anytime soon.
- bravura 6y ago
  Fast throughput rendering yields in-GPU larger datasets, which are useful for ML pretraining.

GuB-42 6y ago

How is it related to ML? Closed form implicit surfaces are actually one of the most difficult way to model the real world. They are neat because they are very compact and the creation process is close to modeling with (mathematical) clay. But they are hard to use if you want to model the real world with all its complexity, resulting of a variety of chemical and physical processes happening over time. There is a reason why they are so popular in the demoscene, for which technical achievement and art is more important than realism, and not much elsewhere. The paper is about making rendering of these primitives more efficient, which may prove to be a great addition to an artist toolbox, and maybe for scientific imagery. However, I don't really see applications for ML anytime soon.

bravura 6y ago
Fast throughput rendering yields in-GPU larger datasets, which are useful for ML pretraining.