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title={Improved techniques for training gans},
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@InProceedings{pmlr-v202-hoogeboom23a,
title = {simple diffusion: End-to-end diffusion for high resolution images},
author = {Hoogeboom, Emiel and Heek, Jonathan and Salimans, Tim},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
pages = {13213--13232},
year = {2023},
volume = {202},
series = {Proceedings of Machine Learning Research},
month = {23--29 Jul},
publisher = {PMLR},
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@article{heusel2017gans,
title={Gans trained by a two time-scale update rule converge to a local nash equilibrium},
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title={Improved Autoregressive Modeling with Distribution Smoothing},
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year={2020}
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title={Distribution augmentation for generative modeling},
author={Jun, Heewoo and Child, Rewon and Chen, Mark and Schulman, John and Ramesh, Aditya and Radford, Alec and Sutskever, Ilya},
booktitle={International Conference on Machine Learning},
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year={2020},
organization={PMLR}
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@inproceedings{choi2022perception,
title={Perception prioritized training of diffusion models},
author={Choi, Jooyoung and Lee, Jungbeom and Shin, Chaehun and Kim, Sungwon and Kim, Hyunwoo and Yoon, Sungroh},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11472--11481},
year={2022}
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@article{hang2023efficient,
title={Efficient diffusion training via min-snr weighting strategy},
author={Hang, Tiankai and Gu, Shuyang and Li, Chen and Bao, Jianmin and Chen, Dong and Hu, Han and Geng, Xin and Guo, Baining},
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year={2023}
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@article{song2021maximum,
title={Maximum likelihood training of score-based diffusion models},
author={Song, Yang and Durkan, Conor and Murray, Iain and Ermon, Stefano},
journal={Advances in Neural Information Processing Systems},
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pages={1415--1428},
year={2021}
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@article{tzen2019neural,
title={Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit},
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journal={arXiv preprint arXiv:1905.09883},
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@article{vahdat2021score,
title={Score-based generative modeling in latent space},
author={Vahdat, Arash and Kreis, Karsten and Kautz, Jan},
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year={2021}
}
@article{huang2021variational,
title={A variational perspective on diffusion-based generative models and score matching},
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year={2021}
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lipman2023flow,
title={Flow Matching for Generative Modeling},
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@inproceedings{
salimans2022progressive,
title={Progressive Distillation for Fast Sampling of Diffusion Models},
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@article{ho2022cascaded,
title={Cascaded diffusion models for high fidelity image generation},
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title = {High Fidelity Image Counterfactuals with Probabilistic Causal Models},
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year = {2023},
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booktitle={International conference on machine learning},
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title={Auxiliary deep generative models},
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pages={1445--1453},
year={2016},
organization={PMLR}
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title={Markov chain monte carlo and variational inference: Bridging the gap},
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title={Measuring axiomatic soundness of counterfactual image models},
author={Monteiro, Miguel and Ribeiro, Fabio De Sousa and Pawlowski, Nick and Castro, Daniel C and Glocker, Ben},
booktitle={The Eleventh International Conference on Learning Representations},
year={2022}
}
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title={Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images},
author={Child, Rewon},
booktitle={International Conference on Learning Representations},
year={2020}
}
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title={Biva: A very deep hierarchy of latent variables for generative modeling},
author={Maal{\o}e, Lars and Fraccaro, Marco and Li{\'e}vin, Valentin and Winther, Ole},
journal={Advances in neural information processing systems},
volume={32},
year={2019}
}
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title={NVAE: A deep hierarchical variational autoencoder},
author={Vahdat, Arash and Kautz, Jan},
journal={Advances in Neural Information Processing Systems},
volume={33},
pages={19667--19679},
year={2020}
}
@article{kingma2019introduction,
title={An introduction to variational autoencoders},
author={Kingma, Diederik P and Welling, Max and others},
journal={Foundations and Trends{\textregistered} in Machine Learning},
volume={12},
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pages={307--392},
year={2019},
publisher={Now Publishers, Inc.}
}
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@inproceedings{hoogeboom2022equivariant,
title={Equivariant diffusion for molecule generation in 3d},
author={Hoogeboom, Emiel and Satorras, V{\i}ctor Garcia and Vignac, Cl{\'e}ment and Welling, Max},
booktitle={International conference on machine learning},
pages={8867--8887},
year={2022},
organization={PMLR}
}
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title={Importance weighted autoencoders},
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journal={arXiv preprint arXiv:1509.00519},
year={2015}
}
@article{song2021train,
title={How to train your energy-based models},
author={Song, Yang and Kingma, Diederik P},
journal={arXiv preprint arXiv:2101.03288},
year={2021}
}
@inproceedings{
salimans2021should,
title={Should {EBM}s model the energy or the score?},
author={Tim Salimans and Jonathan Ho},
booktitle={Energy Based Models Workshop - ICLR 2021},
year={2021},
url={https://openreview.net/forum?id=9AS-TF2jRNb}
}
@article{saharia2022photorealistic,
title={Photorealistic text-to-image diffusion models with deep language understanding},
author={Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily L and Ghasemipour, Kamyar and Gontijo Lopes, Raphael and Karagol Ayan, Burcu and Salimans, Tim and others},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={36479--36494},
year={2022}
}
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title={Reverse-time diffusion equation models},
author={Anderson, Brian DO},
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@inproceedings{nichol2022glide,
title={GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models},
author={Nichol, Alexander Quinn and Dhariwal, Prafulla and Ramesh, Aditya and Shyam, Pranav and Mishkin, Pamela and Mcgrew, Bob and Sutskever, Ilya and Chen, Mark},
booktitle={International Conference on Machine Learning},
pages={16784--16804},
year={2022},
organization={PMLR}
}
@inproceedings{rombach2022high,
title={High-resolution image synthesis with latent diffusion models},
author={Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj{\"o}rn},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={10684--10695},
year={2022}
}
@inproceedings{
song2021scorebased,
title={Score-Based Generative Modeling through Stochastic Differential Equations},
author={Yang Song and Jascha Sohl-Dickstein and Diederik P Kingma and Abhishek Kumar and Stefano Ermon and Ben Poole},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=PxTIG12RRHS}
}
@article{kingma2023understanding,
title={Understanding the diffusion objective as a weighted integral of elbos},
author={Kingma, Diederik P and Gao, Ruiqi},
journal={arXiv preprint arXiv:2303.00848},
year={2023}
}
@inproceedings{nichol2021improved,
title={Improved denoising diffusion probabilistic models},
author={Nichol, Alexander Quinn and Dhariwal, Prafulla},
booktitle={International Conference on Machine Learning},
pages={8162--8171},
year={2021},
organization={PMLR}
}
@article{song2020improved,
title={Improved techniques for training score-based generative models},
author={Song, Yang and Ermon, Stefano},
journal={Advances in neural information processing systems},
volume={33},
pages={12438--12448},
year={2020}
}
@article{karras2022elucidating,
title={Elucidating the design space of diffusion-based generative models},
author={Karras, Tero and Aittala, Miika and Aila, Timo and Laine, Samuli},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={26565--26577},
year={2022}
}
@inproceedings{tomczak2018vae,
title={VAE with a VampPrior},
author={Tomczak, Jakub and Welling, Max},
booktitle={International Conference on Artificial Intelligence and Statistics},
pages={1214--1223},
year={2018},
organization={PMLR}
}
@inproceedings{hoffman2016elbo,
title={Elbo surgery: yet another way to carve up the variational evidence lower bound},
author={Hoffman, Matthew D and Johnson, Matthew J},
booktitle={Workshop in Advances in Approximate Bayesian Inference, NIPS},
volume={1},
year={2016}
}