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Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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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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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, and Yann LeCun · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
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Dynamic few-shot visual learning without forgetting
Spyros Gidaris and Nikos Komodakis · 2018
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Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation
Vladimir Iglovikov and Alexey Shvets · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Ensemble learning: A survey
Omer Sagi and Lior Rokach · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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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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A survey on ensemble learning
Xibin Dong, Zhiwen Yu, Wenming Cao, Yifan Shi, and Qianli Ma · 2020
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A survey on contrastive self-supervised learning
Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 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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Understanding and improving knowledge distillation
Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H Chi, and Sagar Jain · 2020
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Self-supervised pretraining for 2d medical image segmentation
András Kalapos and Bálint Gyires-Tóth · 2022
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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
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Meta knowledge condensation for federated learning
Ping Liu, Xin Yu, and Joey Tianyi Zhou · 2022
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Comparing the performance of pre-trained deep learning models in object detection and recognition
Omar Ibrahim Obaid, Mazin Abed Mohammed, Akbal Omran Salman, Salama A Mostafa, and Ahmed A Elngar · 2022
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Infinite recommendation networks: A data-centric approach
Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, and Julian McAuley · 2022
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Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2020
Cited alongside, same era.
Dataset condensation with gradient matching
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
Cited alongside, same era.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Cited alongside, same era.
Reducing domain gap by reducing style bias
Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo · 2021
Cited alongside, same era.
Dataset distillation with infinitely wide convolutional networks
Timothy Nguyen, Roman Novak, Lechao Xiao, and Jaehoon Lee · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning
Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, and Jiashi Feng · 2021
Cited alongside, same era.
Federated learning via decentralized dataset distillation in resource-constrained edge environments
Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, and Alois Knoll · 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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Feddm: Iterative distribution matching for communication-efficient federated learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2022
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Dataset distillation using neural feature regression
Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba · 2022
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Multifusion: Fusing pre-trained models for multi-lingual, multi-modal image generation
Marco Bellagente, Manuel Brack, Hannah Teufel, Felix Friedrich, Björn Deiseroth, Constantin Eichenberg, Andrew Dai, Robert Baldock, Souradeep Nanda, Koen Oostermeijer, et al · 2023
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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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A comprehensive survey to dataset distillation
Shiye Lei and Dacheng Tao · 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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Gpt-4 technical report, 2023
OpenAI · 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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Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification
Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni · 2023
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Dataset distillation: A comprehensive review
Ruonan Yu, Songhua Liu, and Xinchao Wang · 2023
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Dataset condensation with distribution matching
Bo Zhao and Hakan Bilen · 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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