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Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan S. Yang · 2015
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Visualizing deep convolutional neural networks using natural pre-images
Aravindh Mahendran and Andrea Vedaldi · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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Auto-keras: An efficient neural architecture search system
Haifeng Jin, Qingquan Song, and Xia Hu · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2018
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Selection via proxy: Efficient data selection for deep learning
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia · 2019
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Analyzing and improving the image quality of stylegan
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Narain Sohl-Dickstein, and Jeffrey Pennington · 2019
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Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff A. Bilmes, and Jure Leskovec · 2019
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Continual and multi-task architecture search
Ramakanth Pasunuru and Mohit Bansal · 2019
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Glister: Generalization based data subset selection for efficient and robust learning
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh Iyer University of Texas at Dallas, Indian Institute of Technology Bombay Institution One, and IN Two · 2020
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Reducing catastrophic forgetting with learning on synthetic data
Wojciech Masarczyk and Ivona Tautkute · 2020
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Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourung Chen, and Jaehoon Lee · 2020
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Dataset distillation, 2020
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A. Efros · 2020
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Grad-match: Gradient matching based data subset selection for efficient deep model training
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, and Rishabh K. Iyer · 2021
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Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, A. Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
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Sequential subset matching for dataset distillation
Jiawei Du, Qin Shi, and Joey Tianyi Zhou · 2023
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Efficient dataset distillation via minimax diffusion
Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, and Yiran Chen · 2023
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Towards lossless dataset distillation via difficulty-aligned trajectory matching
Ziyao Guo, Kai Wang, George Cazenavette, Hui Li, Kaipeng Zhang, and Yang You · 2023
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Dataset distillation via the wasserstein metric
Haoyang Liu, Tiancheng Xing, Luwei Li, Vibhu Dalal, Jingrui He, and Haohan Wang · 2023
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Dream: Efficient dataset distillation by representative matching
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Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, and Davide Bacciu · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
Cited alongside, same era.
Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
Cited alongside, same era.
Dataset condensation with distribution matching
Bo Zhao 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
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Scaling up dataset distillation to imagenet-1k with constant memory
Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh · 2022
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Privacy for free: How does dataset condensation help privacy?
Tian Dong, Bo Zhao, and Lingjuan Lyu · 2022
Cited alongside, same era.
Yanqing Liu, Jianyang Gu, Kai Wang, Zheng Hua Zhu, Wei Jiang, and Yang You · 2023
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Dataset distillation with convexified implicit gradients
Noel Loo, Ramin M. Hasani, Mathias Lechner, and Daniela Rus · 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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Loss-curvature matching for dataset selection and condensation
Seung-Jae Shin, Heesun Bae, DongHyeok Shin, Weonyoung Joo, and Il-Chul Moon · 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 · 2023
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Prune then distill: Dataset distillation with importance sampling
Anirudh S. Sundar, Gökçe Keskin, Chander Chandak, I-Fan Chen, Pegah Ghahremani, and Shalini Ghosh · 2023
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Dim: Distilling dataset into generative model
Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Hua Zhu, Wei Jiang, and Yang You · 2023
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Distill gold from massive ores: Efficient dataset distillation via critical samples selection
Yue Xu, Yong-Lu Li, Kaitong Cui, Ziyu Wang, Cewu Lu, Yu-Wing Tai, and Chi-Keung Tang · 2023
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Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective
Zeyuan Yin, Eric P. Xing, and Zhiqiang Shen · 2023
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Multimodal federated learning via contrastive representation ensemble
Qiying Yu, Yang Liu, Yimu Wang, Ke Xu, and Jingjing Liu · 2023
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Dataset distillation: A comprehensive review
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
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Atom: Attention mixer for efficient dataset distillation, 2024
Samir Khaki, Ahmad Sajedi, Kai Wang, Lucy Z. Liu, Yuri A. Lawryshyn, and Konstantinos N. Plataniotis · 2024
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Dataset distillation by automatic training trajectories
Dai Liu, Jindong Gu, Hu Cao, Carsten Trinitis, and Martin Schulz · 2024
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Latent dataset distillation with diffusion models
Brian B. Moser, Federico Raue, Sebastián M. Palacio, Stanislav Frolov, and Andreas Dengel · 2024
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