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Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation (DC), methods can achieve state-of-the-art performance when applied to data-efficient learning tasks.
The mnist database of handwritten digits
LeCun, Y · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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WordNet: An electronic lexical database
Miller, G. A · 1998
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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The german traffic sign recognition benchmark: a multi-class classification competition
Stallkamp, J., Schlipsing, M., Salmen, J., and Igel, C · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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A downsampled variant of imagenet as an alternative to the cifar datasets
Chrabaszcz, P., Loshchilov, I., and Hutter, F · 2017
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
Cited alongside, same era.
Nsml: A machine learning platform that enables you to focus on your models
Sung, N., Kim, M., Jo, H., Yang, Y., Kim, J., Lausen, L., Kim, Y., Lee, G., Kwak, D., Ha, J.-W., et al · 2017
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
Cited alongside, same era.
Dynamic few-shot visual learning without forgetting
Gidaris, S. and Komodakis, N · 2018
Cited alongside, same era.
Data augmentation instead of explicit regularization
Hernández-García, A. and König, P · 2018
Cited alongside, same era.
The break-even point on optimization trajectories of deep neural networks
Jastrzebski, S., Szymczak, M., Fort, S., Arpit, D., Tabor, J., Cho, K., and Geras, K · 2020
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Understanding why neural networks generalize well through gsnr of parameters
Liu, J., Jiang, G., Bai, Y., Chen, T., and Wang, H · 2020
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Coresets for data-efficient training of machine learning models
Mirzasoleiman, B., Bilmes, J., and Leskovec, J · 2020
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Dataset meta-learning from kernel ridge-regression
Nguyen, T., Chen, Z., and Lee, J · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Wang, T. and Isola, P · 2020
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Nsml: Meet the mlaas platform with a real-world case study
Kim, H., Kim, M., Seo, D., Kim, J., Park, H., Park, S., Jo, H., Kim, K., Yang, Y., Kim, Y., et al · 2018
Cited alongside, same era.
Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and Van Der Maaten, L · 2018
Cited alongside, same era.
Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A · 2018
Cited alongside, same era.
On tiny episodic memories in continual learning
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H., and Ranzato, M · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Cited alongside, same era.
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel
Fort, S., Dziugaite, G. K., Paul, M., Kharaghani, S., Roy, D. M., and Ganguli, S · 2020
Cited alongside, same era.
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y., Li, Z., and Duerig, T · 2021
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Characterizing structural regularities of labeled data in overparameterized models
Jiang, Z., Zhang, C., Talwar, K., and Mozer, M. C · 2021
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Removing undesirable feature contributions using out-of-distribution data
Lee, S., Park, C., Lee, H., Yi, J., Lee, J., and Yoon, S · 2021
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Dataset distillation with infinitely wide convolutional networks
Nguyen, T., Novak, R., Xiao, L., and Lee, J · 2021
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Deep learning on a data diet: Finding important examples early in training
Paul, M., Ganguli, S., and Dziugaite, G. K · 2021
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Dataset condensation with differentiable siamese augmentation
Zhao, B. and Bilen, H · 2021
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Dataset condensation with gradient matching
Zhao, B., Mopuri, K. R., and Bilen, H · 2021
Later among the works it cites.