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Although diffusion model has shown great potential for generating higher quality images than GANs, slow sampling speed hinders its wide application in practice.
“Distilling the knowledge in a neural network,”
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al., · 2015
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger, Philipp Fischer, and Thomas Brox, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Improved techniques for training gans,”
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen, · 2016
Earlier work this paper cites.
“Densely connected convolutional networks,”
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, · 2017
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Born again neural networks,”
Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar, · 2018
Earlier work this paper cites.
“Image synthesis with a single (robust) classifier,”
Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry, · 2019
Earlier work this paper cites.
“Denoising diffusion implicit models,”
Jiaming Song, Chenlin Meng, and Stefano Ermon, · 2020
Cited alongside, same era.
“Denoising diffusion probabilistic models,”
Jonathan Ho, Ajay Jain, and Pieter Abbeel, · 2020
Cited alongside, same era.
“Online knowledge distillation with diverse peers,”
Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, and Chun Chen, · 2020
Cited alongside, same era.
“Generative adversarial networks (gans) challenges, solutions, and future directions,”
Divya Saxena and Jiannong Cao, · 2021
Cited alongside, same era.
“Diffusion models beat gans on image synthesis,”
Prafulla Dhariwal and Alexander Nichol, · 2021
Cited alongside, same era.
“Pseudo numerical methods for diffusion models on manifolds,”
Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao, · 2021
Cited alongside, same era.
“On fast sampling of diffusion probabilistic models,”
Zhifeng Kong and Wei Ping, · 2021
Later among the works it cites.
“Knowledge distillation in iterative generative models for improved sampling speed,”
Eric Luhman and Troy Luhman, · 2021
Later among the works it cites.
“Bigroc: Boosting image generation via a robust classifier,”
Roy Ganz and Michael Elad, · 2021
Later among the works it cites.
“Cross-layer distillation with semantic calibration,”
Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Zhe Wang, Yan Feng, and Chun Chen, · 2021
Later among the works it cites.
“Channel-wise knowledge distillation for dense prediction,”
Changyong Shu, Yifan Liu, Jianfei Gao, Zheng Yan, and Chunhua Shen, · 2021
Later among the works it cites.
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“Progressive distillation for fast sampling of diffusion models,”
Tim Salimans and Jonathan Ho, · 2021
Cited alongside, same era.
“Improved denoising diffusion probabilistic models,”
Alexander Quinn Nichol and Prafulla Dhariwal, · 2021
Cited alongside, same era.
Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li, · 2022
Closest in time.
“Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,”
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu, · 2022
Closest in time.
“Knowledge distillation with the reused teacher classifier,”
Defang Chen, Jian-Ping Mei, Hailin Zhang, Can Wang, Yan Feng, and Chun Chen, · 2022
Closest in time.