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While dataset condensation effectively enhances training efficiency, its application in on-device scenarios brings unique challenges.
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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The street view house numbers (svhn) dataset
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Tinytl: Reduce memory, not parameters for efficient on-device learning
Han Cai, Chuang Gan, Ligeng Zhu, and Song Han · 2020
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A survey of on-device machine learning: An algorithms and learning theory perspective
Sauptik Dhar, Junyao Guo, Jiayi Liu, Samarth Tripathi, Unmesh Kurup, and Mohak Shah · 2021
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An overview of energy-efficient hardware accelerators for on-device deep-neural-network training
Jinsu Lee and Hoi-Jun Yoo · 2021
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Dataset condensation with differentiable siamese augmentation
Bo Zhao and Hakan Bilen · 2021
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Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2021
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Dataset distillation by matching training trajectories
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
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Remember the past: Distilling datasets into addressable memories for neural networks
Zhiwei Deng and Olga Russakovsky · 2022
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Delving into effective gradient matching for dataset condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu, and David Z Pan · 2022
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On-device training under 256kb memory
Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, and Song Han · 2022
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Dataset distillation via factorization
Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, and Xinchao Wang · 2022
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Efficient dataset distillation using random feature approximation
Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus · 2022
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Minimizing the accumulated trajectory error to improve dataset distillation
Jiawei Du, Yidi Jiang, Vincent TF Tan, Joey Tianyi Zhou, and Haizhou Li · 2023
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Dataset distillation using parameter pruning
Guang Li, Ren Togo, Takahiro Ogawa, and Miki Haseyama · 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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Loss-curvature matching for dataset selection and condensation
Seungjae 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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Parsa Nooralinejad, Ali Abbasi, Soheil Kolouri, and Hamed Pirsiavash · 2022
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ZeroFL: Efficient on-device training for federated learning with local sparsity
Xinchi Qiu, Javier Fernandez-Marques, Pedro PB Gusmao, Yan Gao, Titouan Parcollet, and Nicholas Donald Lane · 2022
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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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Rep-net: Efficient on-device learning via feature reprogramming
Li Yang, Adnan Siraj Rakin, and Deliang Fan · 2022
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Synthesizing informative training samples with GAN
Bo Zhao and Hakan Bilen · 2022
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 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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Murad Tukan, Alaa Maalouf, and Margarita Osadchy · 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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Efficient on-device training via gradient filtering
Yuedong Yang, Guihong Li, and Radu Marculescu · 2023
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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 · 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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You only condense once: Two rules for pruning condensed datasets
Yang He, Lingao Xiao, and Joey Tianyi Zhou · 2024
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