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Diffusion-based generative models have achieved remarkable success in various domains.
A note on cluster analysis and dynamic programming
Richard Bellman · 1973
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Ckmeans. 1d. dp: optimal k-means clustering in one dimension by dynamic programming
Haizhou Wang and Mingzhou Song · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Optimal interval clustering: Application to bregman clustering and statistical mixture learning
Frank Nielsen and Richard Nock · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2018
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Dynamic task prioritization for multitask learning
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Characterizing and avoiding negative transfer
Zirui Wang, Zihang Dai, Barnabás Póczos, and Jaime Carbonell · 2019
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Understanding and improving information transfer in multi-task learning
Sen Wu, Hongyang R Zhang, and Christopher Ré · 2019
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Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Which tasks should be learned together in multi-task learning?
Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2020
Cited alongside, same era.
Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Diffusionclip: Text-guided diffusion models for robust image manipulation
Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye · 2022
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In defense of the unitary scalarization for deep multi-task learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, and Pawan K Mudigonda · 2022
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Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto · 2022
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A closer look at loss weighting in multi-task learning, 2022
Baijiong Lin, Feiyang YE, and Yu Zhang · 2022
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SDEdit: Guided image synthesis and editing with stochastic differential equations
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Efficiently identifying task groupings for multi-task learning
Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, and Chelsea Finn · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Cited alongside, same era.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
On fast sampling of diffusion probabilistic models
Zhifeng Kong and Wei Ping · 2021
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Cited alongside, same era.
Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Cited alongside, same era.
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2022
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Multi-task learning as a bargaining game
Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya · 2022
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen · 2022
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On aliased resizing and surprising subtleties in gan evaluation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Efficient and effective multi-task grouping via meta learning on task combinations
Xiaozhuang Song, Shun Zheng, Wei Cao, James Yu, and Jiang Bian · 2022
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Do current multi-task optimization methods in deep learning even help?
Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, and Orhan Firat · 2022
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Towards practical plug-and-play diffusion models
Hyojun Go, Yunsung Lee, Jin-Young Kim, Seunghyun Lee, Myeongho Jeong, Hyun Seung Lee, and Seungtaek Choi · 2023
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Efficient diffusion training via min-snr weighting strategy
Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo · 2023
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Forkmerge: Overcoming negative transfer in multi-task learning
Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Liu Dapeng, Jie Jiang, and Mingsheng Long · 2023
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Multi-architecture multi-expert diffusion models
Yunsung Lee, Jin-Young Kim, Hyojun Go, Myeongho Jeong, Shinhyeok Oh, and Seungtaek Choi · 2023
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Libmtl: A python library for deep multi-task learning
Baijiong Lin and Yu Zhang · 2023
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Denoising task routing for diffusion models
Byeongjun Park, Sangmin Woo, Hyojun Go, Jin-Young Kim, and Changick Kim · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Sdxl: improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
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Stable target field for reduced variance score estimation in diffusion models
Yilun Xu, Shangyuan Tong, and Tommi S. Jaakkola · 2023
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Diffusion probabilistic model made slim
Xingyi Yang, Daquan Zhou, Jiashi Feng, and Xinchao Wang · 2023
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