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The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them.
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
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Super-samples from kernel herding
Yutian Chen, Max Welling, and Alex Smola · 2010
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Facility location: concepts, models, algorithms and case studies. series: Contributions to management science, 2011
Gert W Wolf · 2011
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The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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Deep bayesian active learning with image data
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Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
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Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frederic Precioso · 2018
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Ozan Sener and Silvio Savarese · 2018
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Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, and Animashree Anandkumar · 2018
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Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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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 · 2019
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Lucas Beyer, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord · 2020
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Coresets via bilevel optimization for continual learning and streaming
Zalán Borsos, Mojmir Mutny, and Andreas Krause · 2020
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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 · 2020
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Introduction to core-sets: an updated survey
Dan Feldman · 2020
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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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Deepcore: A comprehensive library for coreset selection in deep learning
Chengcheng Guo, Bo Zhao, and Yanbing Bai · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 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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Simmim: A simple framework for masked image modeling
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu · 2022
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Natural adversarial examples
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Active learning by acquiring contrastive examples
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Dataset meta-learning from kernel-ridge regression
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Synthesizing informative training samples with GAN
Bo Zhao and Hakan Bilen · 2022
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Scaling up dataset distillation to imagenet-1k with constant memory
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Eva: Exploring the limits of masked visual representation learning at scale
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Dataset pruning: Reducing training data by examining generalization influence
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Data-efficient neural network training with dataset condensation
Bo Zhao · 2023
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Dataset condensation with distribution matching
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Infobatch: Lossless training speed up by unbiased dynamic data pruning
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