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In recent years, the attention towards One-Shot Federated Learning (OSFL) has been driven by its capacity to minimize communication.
The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol. 2021 · 2021
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Variational Diffusion Models. In Advances in Neural Information Processing Systems , M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34. Curran Associates, Inc., 21696–21707
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Fedbn: Federated learning on non-iid features via local batch normalization
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
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Learning transferable visual models from natural language supervision. In International conference on machine learning . PMLR, 8748–8763
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Training-free structured diffusion guidance for compositional text-to-image synthesis
Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang. 2022 · 2022
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An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. 2022 · 2022
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Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity. In The Eleventh International Conference on Learning Representations
Clare Elizabeth Heinbaugh, Emilio Luz-Ricca, and Huajie Shao. 2022 · 2022
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Diffusionclip: Text-guided diffusion models for robust image manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2426–2435
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Nico++: Towards better benchmarking for domain generalization
Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, and Haoxin Liu. 2022b · 2022
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Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
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Svdiff: Compact parameter space for diffusion fine-tuning
Ligong Han, Yinxiao Li, Han Zhang, Peyman Milanfar, Dimitris Metaxas, and Feng Yang. 2023 · 2023
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Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023a · 2023
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Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao. 2022b · 2022
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Compositional visual generation with composable diffusion models. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVII . Springer, 423–439
Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum. 2022a · 2022
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Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring. In ICML (Proceedings of Machine Learning Research, Vol. 162) . PMLR, 14527–14541
Zhengquan Luo, Yunlong Wang, Zilei Wang, Zhenan Sun, and Tieniu Tan. 2022 · 2022
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Fine-tuning Diffusion Models with Limited Data. In NeurIPS 2022 Workshop on Score-Based Methods
Taehong Moon, Moonseok Choi, Gayoung Lee, Jung-Woo Ha, and Juho Lee. 2022 · 2022
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Diffusion autoencoders: Toward a meaningful and decodable representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10619–10629
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn. 2022 · 2022
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High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
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Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 Conference Proceedings . 1–10
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. 2022a · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds. In Advances in Neural Information Processing Systems , A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds.), Vol. 36. Curran Associates, Inc., 20662–20678
Yanyu Li, Huan Wang, Qing Jin, Ju Hu, Pavlo Chemerys, Yun Fu, Yanzhi Wang, Sergey Tulyakov, and Jian Ren. 2023b · 2023
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Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi, Madan Ravi Ganesh, Zhenzhen Li, Lu Peng, and Wan-Yi Lin. 2023 · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 22500–22510
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023 · 2023
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One-shot Federated Learning without server-side training
Shangchao Su, Bin Li, and Xiangyang Xue. 2023 · 2023
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BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion
Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. 2023 · 2023
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Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model
Mingzhao Yang, Shangchao Su, Bin Li, and Xiangyang Xue. 2023a · 2023
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One-Shot Federated Learning with Classifier-Guided Diffusion Models
Mingzhao Yang, Shangchao Su, Bin Li, and Xiangyang Xue. 2023b · 2023
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Federated generative learning with foundation models
Jie Zhang, Xiaohua Qi, and Bo Zhao. 2023 · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala. 2023 · 2023
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Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 38. 15117–15125
Shangchao Su, Mingzhao Yang, Bin Li, and Xiangyang Xue. 2024 · 2024
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Exploiting shared representations for personalized federated learning. In International conference on machine learning . PMLR, 2089–2099
Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. 2021 · 2099
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