MinVIS
Cold-Diffusion-Models
MinVIS | Cold-Diffusion-Models | |
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5 | 14 | |
261 | 933 | |
0.0% | - | |
0.0 | 0.0 | |
over 1 year ago | over 1 year ago | |
Python | Python | |
GNU General Public License v3.0 or later | - |
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MinVIS
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Using image based models for videos
This is basically that but for instance segmentation https://github.com/NVlabs/MinVIS
- [D] Most Popular AI Research Aug 2022 - Ranked Based On GitHub Stars
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Most Popular AI Research Aug 2022 pt. 2 - Ranked Based On GitHub Stars
https://arxiv.org/abs/2208.02245 https://github.com/nvlabs/minvis
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NVIDIA AI Researchers Propose ‘MinVIS,’ A Minimal Video Instance Segmentation (VIS) Framework That Achieves SOTA Performance With Neither Video-Based Architectures Nor Training Procedures
Continue reading | Check out the Preprint/Under review paper and github link.
- A Minimal Video Instance Segmentation Framework Without Video-Based Training
Cold-Diffusion-Models
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[Discussion] training a diffusion model with a destructive process other than gaussian noise
Sure you can. You might be interested in cold diffusion (https://arxiv.org/abs/2208.09392) which tries doing a bunch of different kinds of degradation processes besides adding gaussian noise. You can kind of choose whatever input corruption process you want and teach the model to reverse it, and it works kinda well (I think gaussian noise might be better though)
- [D]eterministic diffusion models
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The Uncanny Failures of A.I.-Generated Hands
I wrote a response yesterday but did not post it or send it, ops.
I still don't understand the problem, if you ask model trained on a noise pattern "trees" for a forest it will still give you a random forest, that's what it was trained on, also: https://arxiv.org/abs/2208.09392, to see the diffusion process applied to processes other than Gaussian noise.
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when will be get away from noise based diffusion
What about this research: https://arxiv.org/abs/2208.09392
- Becoming a machine learning Engineer.
- About art AIs, how noise works?
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Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
Found relevant code at https://github.com/arpitbansal297/Cold-Diffusion-Models + all code implementations here
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Denoising Diffusion models from first principle in Julia
This claims to explain diffusion models from first principles, but the issue with explaining how they work is we don't know how they work.
The explanation in the original paper turns out not to be true; you can get rid of most of their assumptions and it still works: https://arxiv.org/abs/2208.09392
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[D] Has anyone tried coding latent diffusion from scratch? or tried other conditioning information aside from image classes and text?
Check out the cold-diffusion repo, which has nice clean implementations, and also is useful in pointing out that the multi-step computation idea isn’t limited to denoising. https://github.com/arpitbansal297/Cold-Diffusion-Models
- [D] Most Popular AI Research August 2022 - Ranked By Twitter Likes
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