@librarian-bot\n\t could you help me with similar papers to read?

\n","updatedAt":"2024-05-06T05:04:03.474Z","author":{"_id":"608aabf24955d2bfc3cd99c6","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/608aabf24955d2bfc3cd99c6/T762Ut0Y-w0sZB2ynvfbJ.jpeg","fullname":"Aritra Roy Gosthipaty","name":"ariG23498","type":"user","isPro":true,"isHf":true,"isHfAdmin":false,"isMod":false,"followerCount":364}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9769827723503113},"editors":["ariG23498"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/608aabf24955d2bfc3cd99c6/T762Ut0Y-w0sZB2ynvfbJ.jpeg"],"reactions":[],"isReport":false}},{"id":"663a28d4662ad3bf0b297091","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":264},"createdAt":"2024-05-07T13:12:52.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Lossy Image Compression with Foundation Diffusion Models](https://huggingface.co/papers/2404.08580) (2024)\n* [Laplacian-guided Entropy Model in Neural Codec with Blur-dissipated Synthesis](https://huggingface.co/papers/2403.16258) (2024)\n* [Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models](https://huggingface.co/papers/2404.16556) (2024)\n* [Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling](https://huggingface.co/papers/2404.12940) (2024)\n* [Physics-Informed Diffusion Models](https://huggingface.co/papers/2403.14404) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`","html":"

This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

The following papers were recommended by the Semantic Scholar API

Please give a thumbs up to this comment if you found it helpful!

If you want recommendations for any Paper on Hugging Face checkout this Space

You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: \n\n@librarian-bot\n\t recommend

\n","updatedAt":"2024-05-07T13:12:52.311Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":264}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7338171601295471},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[{"reaction":"👍","users":["ariG23498","uvikas"],"count":2},{"reaction":"😎","users":["ariG23498"],"count":1}],"isReport":false}},{"id":"66654798a7ce1f4ec857b9d3","author":{"_id":"6186ddf6a7717cb375090c01","avatarUrl":"/avatars/716b6a7d1094c8036b2a8a7b9063e8aa.svg","fullname":"Julien BLANCHON","name":"blanchon","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":142},"createdAt":"2024-06-09T06:11:36.000Z","type":"comment","data":{"edited":false,"hidden":false,"latest":{"raw":"# Revolutionizing Image Generation with Denoising Diffusion Models!\n\nhttps://cdn-uploads.huggingface.co/production/uploads/6186ddf6a7717cb375090c01/b5zWSGyMhfHavb1GAZyWy.mp4 \n\n## Links 🔗:\n👉 Subscribe: https://www.youtube.com/@Arxflix\n👉 Twitter: https://x.com/arxflix\n👉 LMNT (Partner): https://lmnt.com/\n\n\nBy Arxflix\n![9t4iCUHx_400x400-1.jpg](https://cdn-uploads.huggingface.co/production/uploads/6186ddf6a7717cb375090c01/v4S5zBurs0ouGNwYj1GEd.jpeg)","html":"

Revolutionizing Image Generation with Denoising Diffusion Models!

\n\n

Links 🔗:

👉 Subscribe: https://www.youtube.com/@Arxflix
👉 Twitter: https://x.com/arxflix
👉 LMNT (Partner): https://lmnt.com/

By Arxflix
$\"9t4iCUHx_400x400-1.jpg\"$

\n","updatedAt":"2024-06-09T06:11:36.441Z","author":{"_id":"6186ddf6a7717cb375090c01","avatarUrl":"/avatars/716b6a7d1094c8036b2a8a7b9063e8aa.svg","fullname":"Julien BLANCHON","name":"blanchon","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":142}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.4742056131362915},"editors":["blanchon"],"editorAvatarUrls":["/avatars/716b6a7d1094c8036b2a8a7b9063e8aa.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2006.11239","authors":[{"_id":"6411c77d6b75ddced38902b3","name":"Jonathan Ho","hidden":false},{"_id":"6411c77d6b75ddced38902b4","user":{"_id":"62f678c42e53c2efd33c23a0","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/QlVjKgk2QEA4Z20WBCHMV.png","isPro":false,"fullname":"Ajay Jain","user":"ajayj","type":"user"},"name":"Ajay Jain","status":"extracted_pending","statusLastChangedAt":"2023-03-15T09:40:26.587Z","hidden":false},{"_id":"6411c77d6b75ddced38902b5","name":"Pieter Abbeel","hidden":false}],"publishedAt":"2020-06-19T17:24:44.000Z","title":"Denoising Diffusion Probabilistic Models","summary":"We present high quality image synthesis results using diffusion probabilistic\nmodels, a class of latent variable models inspired by considerations from\nnonequilibrium thermodynamics. Our best results are obtained by training on a\nweighted variational bound designed according to a novel connection between\ndiffusion probabilistic models and denoising score matching with Langevin\ndynamics, and our models naturally admit a progressive lossy decompression\nscheme that can be interpreted as a generalization of autoregressive decoding.\nOn the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and\na state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality\nsimilar to ProgressiveGAN. Our implementation is available at\nhttps://github.com/hojonathanho/diffusion","upvotes":6,"discussionId":"641192343ea54b1aa7e2f083","ai_summary":"Diffusion probabilistic models achieve high-quality image synthesis through novel training and lossy decompression techniques, outperforming existing methods on CIFAR10 and LSUN datasets.","ai_keywords":["diffusion probabilistic models","latent variable models","nonequilibrium thermodynamics","weighted variational bound","denoising score matching","Langevin dynamics","progressive lossy decompression","autoregressive decoding","Inception score","FID score","ProgressiveGAN"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"6316c9e193ab42acfb0d2599","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6316c9e193ab42acfb0d2599/Z_iugzOnx-Qbx7SwBD4TC.jpeg","isPro":false,"fullname":"Steven Casteel","user":"stevencasteel","type":"user"},{"_id":"6538119803519fddb4a17e10","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6538119803519fddb4a17e10/ffJMkdx-rM7VvLTCM6ri_.jpeg","isPro":false,"fullname":"samusenps","user":"samusenps","type":"user"},{"_id":"608aabf24955d2bfc3cd99c6","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/608aabf24955d2bfc3cd99c6/T762Ut0Y-w0sZB2ynvfbJ.jpeg","isPro":true,"fullname":"Aritra Roy Gosthipaty","user":"ariG23498","type":"user"},{"_id":"67ba3fc00c4320b0e6068e68","avatarUrl":"/avatars/c92a7e371f45f8960f8e9077d64a0f95.svg","isPro":false,"fullname":"Abdul","user":"lagenderier107","type":"user"},{"_id":"63c6cb6a50cc81901da65e15","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63c6cb6a50cc81901da65e15/t4LN1BPCFlwbSJ9GD9YDd.jpeg","isPro":true,"fullname":"Théo Pomies","user":"theopomies","type":"user"},{"_id":"67ea85633d9d13966606b843","avatarUrl":"/avatars/dbf505a63c4c4a285ed85611b9138915.svg","isPro":false,"fullname":"Byungsoo Kang","user":"kbsooo","type":"user"}],"acceptLanguages":["*"]}">

Papers

arxiv:2006.11239

Denoising Diffusion Probabilistic Models

Published on Jun 19, 2020

Upvote

Authors:

Ajay Jain ,

Abstract

Diffusion probabilistic models achieve high-quality image synthesis through novel training and lossy decompression techniques, outperforming existing methods on CIFAR10 and LSUN datasets.

AI-generated summary

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion