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What does a neural network learn when training from a task-specific dataset? Synthesizing this knowledge is the central idea behind Dataset Distillation, which recent work has shown can be used to compress large datasets into a small set of input-label pairs ($\textit{prototypes}$) that capture essential aspects of the original dataset.
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CAFE: Learning to condense dataset by aligning FEatures
Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You · 2022
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Synthesizing informative training samples with GAN
Bo Zhao and Hakan Bilen · 2022
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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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Dataset distillation via the wasserstein metric
Haoyang Liu, Tiancheng Xing, Luwei Li, Vibhu Dalal, Jingrui He, and Haohan Wang · 2023
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A Sajedi, Samir Khaki, Ehsan Amjadian, Lucy Z Liu, Y Lawryshyn, and K Plataniotis · 2023
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Dataset distillation in large data era
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Squeeze, recover and relabel: Dataset condensation at ImageNet scale from a new perspective
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