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The extensive amounts of data required for training deep neural networks pose significant challenges on storage and transmission fronts.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Openimages: A public dataset for large-scale multi-label and multi-class image classification
Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Shahab Kamali, Matteo Malloci, Jordi Pont-Tuset, Andreas Veit, Serge Belongie, Victor Gomes, Abhinav Gupta, Chen Sun, Gal Chechik, David Cai, Zheyun Feng, Dhyanesh Narayanan, and Kevin Murphy · 2017
Earlier work this paper cites.
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Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Earlier work this paper cites.
An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
Earlier work this paper cites.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Neural tangents: Fast and easy infinite neural networks in python
Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A Alemi, Jascha Sohl-Dickstein, and Samuel S Schoenholz · 2019
Earlier work this paper cites.
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Coresets for data-efficient training of machine learning models
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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
A unified approach to coreset learning
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Generalizing dataset distillation via deep generative prior
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu · 2023
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Scaling up dataset distillation to imagenet-1k with constant memory
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On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm
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Towards lossless dataset distillation via difficulty-aligned trajectory matching
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Learning transferable visual models from natural language supervision
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High-resolution image synthesis with latent diffusion models
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Ziyao Guo, Kai Wang, George Cazenavette, Hui Li, Kaipeng Zhang, and Yang You · 2023
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Generalized large-scale data condensation via various backbone and statistical matching
Shitong Shao, Zeyuan Yin, Muxin Zhou, Xindong Zhang, and Zhiqiang Shen · 2023
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Dataset quantization
Daquan Zhou, Kai Wang, Jianyang Gu, Xiangyu Peng, Dongze Lian, Yifan Zhang, Yang You, and Jiashi Feng · 2023
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Accelerating dataset distillation via model augmentation
Lei Zhang, Jie Zhang, Bowen Lei, Subhabrata Mukherjee, Xiang Pan, Bo Zhao, Caiwen Ding, Yao Li, and Dongkuan Xu · 2023
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Bo Zhao and Hakan Bilen · 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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Improved distribution matching for dataset condensation
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Dataset distillation in large data era
Zeyuan Yin and Zhiqiang Shen · 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 · 2024
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Data distillation can be like vodka: Distilling more times for better quality
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