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Dataset Distillation has emerged as a technique for compressing large datasets into smaller synthetic counterparts, facilitating downstream training tasks.
Least squares quantization in pcm
Lloyd, S · 1982
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
The mnist database of handwritten digit images for machine learning research
Deng, L · 2012
Earlier work this paper cites.
A deeper look at dataset bias
Tommasi, T., Patricia, N., Caputo, B., and Tuytelaars, T · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
Earlier work this paper cites.
Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A · 2018
Earlier work this paper cites.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations, Mar 2019
Hendrycks, D. and Dietterich, T · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Earlier work this paper cites.
Repair: Removing representation bias by dataset resampling
Li, Y. and Vasconcelos, N · 2019
Earlier work this paper cites.
Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
Earlier work this paper cites.
Benchmarking Attribution Methods with Relative Feature Importance
Yang, M. and Kim, B · 2019
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
Earlier work this paper cites.
Supervised contrastive learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D · 2020
Earlier work this paper cites.
Learning from failure: De-biasing classifier from biased classifier
Nam, J., Cha, H., Ahn, S., Lee, J., and Shin, J · 2020
Earlier work this paper cites.
Dataset meta-learning from kernel ridge-regression
Nguyen, T., Chen, Z., and Lee, J · 2020
Earlier work this paper cites.
Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data
Such, F. P., Rawal, A., Lehman, J., Stanley, K., and Clune, J · 2020
Earlier work this paper cites.
Graph condensation for graph neural networks
Jin, W., Zhao, L., Zhang, S., Liu, Y., Tang, J., and Shah, N · 2021
Earlier work this paper cites.
Learning debiased representation via disentangled feature augmentation
Lee, J., Kim, E., Lee, J., Lee, J., and Choo, J · 2021
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Learning to generate synthetic training data using gradient matching and implicit differentiation
Medvedev, D. and D’yakonov, A · 2021
Cited alongside, same era.
Rethinking architecture selection in differentiable nas
Wang, R., Cheng, M., Chen, X., Tang, X., and Hsieh, C.-J · 2021
Cited alongside, same era.
Dataset distillation by matching training trajectories
Cazenavette, G., Wang, T., Torralba, A., Efros, A. A., and Zhu, J.-Y · 2022
Cited alongside, same era.
Remember the past: Distilling datasets into addressable memories for neural networks
Deng, Z. and Russakovsky, O · 2022
Cited alongside, same era.
Selecmix: Debiased learning by contradicting-pair sampling
Hwang, I., Lee, S., Kwak, Y., Oh, S. J., Teney, D., Kim, J.-H., and Zhang, B.-T · 2022
Few-shot dataset distillation via translative pre-training
Liu, S. and Wang, X · 2023
Later among the works it cites.
Dream: Efficient dataset distillation by representative matching
Liu, Y., Gu, J., Wang, K., Zhu, Z., Jiang, W., and You, Y · 2023
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Dataset distillation with convexified implicit gradients
Loo, N., Hasani, R., Lechner, M., and Rus, D · 2023
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Datadam: Efficient dataset distillation with attention matching
Sajedi, A., Khaki, S., Amjadian, E., Liu, L. Z., Lawryshyn, Y. A., and Plataniotis, K. N · 2023
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On the diversity and realism of distilled dataset: An efficient dataset distillation paradigm
Sun, P., Shi, B., Yu, D., and Lin, T · 2023
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Cited alongside, same era.
Dataset condensation via efficient synthetic-data parameterization
Kim, J.-H., Kim, J., Oh, S. J., Yun, S., Song, H., Jeong, J., Ha, J.-W., and Song, H. O · 2022
Cited alongside, same era.
Dataset condensation with latent space knowledge factorization and sharing
Lee, H. B., Lee, D. B., and Hwang, S. J · 2022
Cited alongside, same era.
Awesome dataset distillation
Li, G., Zhao, B., and Wang, T · 2022
Cited alongside, same era.
Dataset distillation via factorization
Liu, S., Wang, K., Yang, X., Ye, J., and Wang, X · 2022
Cited alongside, same era.
Efficient dataset distillation using random feature approximation
Loo, N., Hasani, R., Amini, A., and Rus, D · 2022
Cited alongside, same era.
Cafe: Learning to condense dataset by aligning features
Wang, K., Zhao, B., Peng, X., Zhu, Z., Yang, S., Wang, S., Huang, G., Bilen, H., Wang, X., and You, Y · 2022
Cited alongside, same era.
Dim: Distilling dataset into generative model
Wang, K., Gu, J., Zhou, D., Zhu, Z., Jiang, W., and You, Y · 2023
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Multimodal dataset distillation for image-text retrieval
Wu, X., Deng, Z., and Russakovsky, O · 2023
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Feddm: Iterative distribution matching for communication-efficient federated learning
Xiong, Y., Wang, R., Cheng, M., Yu, F., and Hsieh, C.-J · 2023
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An efficient dataset condensation plugin and its application to continual learning
Yang, E., Shen, L., Wang, Z., Liu, T., and Guo, G · 2023
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Dataset condensation via generative model
Zhang, D. J., Wang, H., Xue, C., Yan, R., Zhang, W., Bai, S., and Shou, M. Z · 2023
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Dataset condensation with distribution matching
Zhao, B. and Bilen, H · 2023
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Improved distribution matching for dataset condensation
Zhao, G., Li, G., Qin, Y., and Yu, Y · 2023
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Dataset distillation using neural feature regression
Zhou, Y., Nezhadarya, E., and Ba, J · 2023
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Sequential subset matching for dataset distillation
Du, J., Shi, Q., and Zhou, J. T · 2024
Closest in time.
Graph data condensation via self-expressive graph structure reconstruction
Liu, Z., Zeng, C., and Zheng, G · 2024
Closest in time.
Frequency domain-based dataset distillation
Shin, D., Shin, S., and Moon, I.-C · 2024
Closest in time.
Sparse parameterization for epitomic dataset distillation
Wei, X., Cao, A., Yang, F., and Ma, Z · 2024
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Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective
Yin, Z., Xing, E., and Shen, Z · 2024
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