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Diffusion models (DMs) have demonstrated exceptional generative capabilities across various domains, including image, video, and so on.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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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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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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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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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Data-free learning of student networks
Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian · 2019
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Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu · 2019
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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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Zero-shot knowledge transfer via adversarial belief matching
Paul Micaelli and Amos J Storkey · 2019
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Zero-shot knowledge distillation in deep networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty · 2019
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Relational knowledge distillation
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
V Sanh · 2019
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Knowledge extraction with no observable data
Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang · 2019
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Data-free network quantization with adversarial knowledge distillation
Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Local correlation consistency for knowledge distillation
Xiaojie Li, Jianlong Wu, Hongyu Fang, Yue Liao, Fei Wang, and Chen Qian · 2020
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Large-scale generative data-free distillation
Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, and Andrew Howard · 2020
Earlier work this paper cites.
Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Cited alongside, same era.
Contrastive model inversion for data-free knowledge distillation
Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang, and Mingli Song · 2021
Cited alongside, same era.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Cited alongside, same era.
Noise2music: Text-conditioned music generation with diffusion models
Qingqing Huang, Daniel S Park, Tao Wang, Timo I Denk, Andy Ly, Nanxin Chen, Zhengdong Zhang, Zhishuai Zhang, Jiahui Yu, Christian Frank, et al · 2023
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Scaling up gans for text-to-image synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park · 2023
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Mask again: Masked knowledge distillation for masked video modeling
Xiaojie Li, Shaowei He, Jianlong Wu, Yue Yu, Liqiang Nie, and Min Zhang · 2023
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On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
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Input perturbation reduces exposure bias in diffusion models
Mang Ning, Enver Sangineto, Angelo Porrello, Simone Calderara, and Rita Cucchiara · 2023
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Eric Luhman and Troy Luhman · 2021
Cited alongside, same era.
Generating images with sparse representations
Charlie Nash, Jacob Menick, Sander Dieleman, and Peter W. Battaglia · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, et al · 2022
Cited alongside, same era.
Robust and resource-efficient data-free knowledge distillation by generative pseudo replay
Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham, Karianto Leman, and Tulika Mitra · 2022
Cited alongside, same era.
Up to 100x faster data-free knowledge distillation
Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, and Mingli Song · 2022
Cited alongside, same era.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Dynamic contrastive distillation for image-text retrieval
Jun Rao, Liang Ding, Shuhan Qi, Meng Fang, Yang Liu, Li Shen, and Dacheng Tao · 2023
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Adversarial diffusion distillation
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach · 2023
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Consistency models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Datasetdm: Synthesizing data with perception annotations using diffusion models
Weijia Wu, Yuzhong Zhao, Hao Chen, Yuchao Gu, Rui Zhao, Yefei He, Hong Zhou, Mike Zheng Shou, and Chunhua Shen · 2023
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Data-free knowledge distillation via feature exchange and activation region constraint
Shikang Yu, Jiachen Chen, Hu Han, and Shuqiang Jiang · 2023
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Mobilediffusion: Subsecond text-to-image generation on mobile devices
Yang Zhao, Yanwu Xu, Zhisheng Xiao, and Tingbo Hou · 2023
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Genview: Enhancing view quality with pretrained generative model for self-supervised learning
Xiaojie Li, Yibo Yang, Xiangtai Li, Jianlong Wu, Yue Yu, Bernard Ghanem, and Min Zhang · 2024
Closest in time.
Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation
Quang Nguyen, Truong Vu, Anh Tran, and Khoi Nguyen · 2024
Closest in time.
Fast high-resolution image synthesis with latent adversarial diffusion distillation
Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach · 2024
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Stablerep: Synthetic images from text-to-image models make strong visual representation learners
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, and Dilip Krishnan · 2024
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Em distillation for one-step diffusion models
Sirui Xie, Zhisheng Xiao, Diederik P Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, and Ruiqi Gao · 2024
Closest in time.
Clip-kd: An empirical study of clip model distillation
Chuanguang Yang, Zhulin An, Libo Huang, Junyu Bi, Xinqiang Yu, Han Yang, Boyu Diao, and Yongjun Xu · 2024
Closest in time.
Laptop-diff: Layer pruning and normalized distillation for compressing diffusion models
Dingkun Zhang, Sijia Li, Chen Chen, Qingsong Xie, and Haonan Lu · 2024
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