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Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the original large-scale one.
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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Scalarizations for adaptively solving multi-objective optimization problems
Gabriele Eichfelder · 2009
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Herding dynamical weights to learn
Max Welling · 2009
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Representation formula for the entropy and functional inequalities
Joseph Lehec · 2013
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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DeLiGAN: Generative Adversarial Networks for Diverse and Limited Data
Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R. Venkatesh Babu · 2017
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Regularization under diffusion and anticoncentration of the information content
Ronen Eldan and James R Lee · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, and Honglak Lee · 2018
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Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, and Ming-Hsuan Yang · 2019
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Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen and Maxim Raginsky · 2019
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This Dataset Does Not Exist: Training Models from Generated Images
Victor Besnier, Himalaya Jain, Andrei Bursuc, Matthieu Cord, and Patrick Pérez · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Variational Diffusion Models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Improved Denoising Diffusion Probabilistic Models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Trilevel and multilevel optimization using monotone operator theory
Allahkaram Shafiei, Vyacheslav Kungurtsev, and Jakub Marecek · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
Cited alongside, same era.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
Cited alongside, same era.
Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2022
Cited alongside, same era.
Dc-bench: Dataset condensation benchmark
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2022
Cited alongside, same era.
Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
Diversify your vision datasets with automatic diffusion-based augmentation
Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph E Gonzalez, and Trevor Darrell · 2023
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Summarizing stream data for memory-restricted online continual learning
Jianyang Gu, Kai Wang, Wei Jiang, and Yang You · 2023
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Dream: Efficient dataset distillation by representative matching
Yanqing Liu, Jianyang Gu, Kai Wang, Zheng Zhu, Wei Jiang, and Yang You · 2023
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Dataset distillation with convexified implicit gradients
Noel Loo, Ramin Hasani, Mathias Lechner, and Daniela Rus · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Zhiwei Deng and Olga Russakovsky · 2022
Cited alongside, same era.
Nonlinear two-time-scale stochastic approximation convergence and finite-time performance
Thinh T Doan · 2022
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 · 2022
Cited alongside, same era.
Is Synthetic Data from Generative Models Ready for Image Recognition?
Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and Xiaojuan Qi · 2022
Cited alongside, same era.
Dataset condensation via efficient synthetic-data parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song · 2022
Cited alongside, same era.
Dataset condensation with contrastive signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, and Sungroh Yoon · 2022
Cited alongside, same era.
Meta Knowledge Condensation for Federated Learning
Ping Liu, Xin Yu, and Joey Tianyi Zhou · 2022
Cited alongside, same era.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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DataDAM: Efficient Dataset Distillation with Attention Matching
Ahmad Sajedi, Samir Khaki, Ehsan Amjadian, Lucy Z Liu, Yuri A Lawryshyn, and Konstantinos N Plataniotis · 2023
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Fake it Till You Make it: Learning Transferable Representations from Synthetic ImageNet Clones
Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, and Yannis Kalantidis · 2023
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Diversity Is Definitely Needed: Improving Model-Agnostic Zero-Shot Classification via Stable Diffusion
Jordan Shipard, Arnold Wiliem, Kien Nguyen Thanh, Wei Xiang, and Clinton Fookes · 2023
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Dim: Distilling dataset into generative model
Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu, Wei Jiang, and Yang You · 2023
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Multimodal dataset distillation for image-text retrieval
Xindi Wu, Zhiwei Deng, and Olga Russakovsky · 2023
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Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, and Zhenguo Li · 2023
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FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2023
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Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective
Zeyuan Yin, Eric Xing, and Zhiqiang Shen · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 2023
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Improved Distribution Matching for Dataset Condensation
Ganlong Zhao, Guanbin Li, Yipeng Qin, and Yizhou Yu · 2023
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Training on thin air: Improve image classification with generated data
Yongchao Zhou, Hshmat Sahak, and Jimmy Ba · 2023
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On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm
Peng Sun, Bei Shi, Daiwei Yu, and Tao Lin · 2024
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Group distributionally robust dataset distillation with risk minimization
Saeed Vahidian, Mingyu Wang, Jianyang Gu, Vyacheslav Kungurtsev, Wei Jiang, and Yiran Chen · 2024
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