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Dataset distillation (DD) allows datasets to be distilled to fractions of their original size while preserving the rich distributional information, so that models trained on the distilled datasets can achieve a comparable accuracy while saving significant computational loads.
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Wong, E.; Schmidt, F. R.; and Kolter, J. Z. 2019 · 1902
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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 · 2009
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Dataset meta-learning from kernel ridge-regression
Nguyen, T.; Chen, Z.; and Lee, J. 2020 · 2011
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Towards Deep Neural Network Architectures Robust to Adversarial Examples
Gu, S.; and Rigazio, L. 2014 · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2015 · 2015
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Tiny imagenet visual recognition challenge
Le, Y.; and Yang, X. 2015 · 2015
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Distributional Smoothing with Virtual Adversarial Training
Miyato, T.; ichi Maeda, S.; Koyama, M.; Nakae, K.; and Ishii, S. 2015 · 2015
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M.; Fawzi, A.; and Frossard, P. 2016 · 2016
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Simple Black-Box Adversarial Perturbations for Deep Networks
Narodytska, N.; and Kasiviswanathan, S. P. 2016 · 2016
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Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Papernot, N.; McDaniel, P.; and Goodfellow, I. 2016 · 2016
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The limitations of deep learning in adversarial settings
Papernot, N.; McDaniel, P.; Jha, S.; Fredrikson, M.; Celik, Z. B.; and Swami, A. 2017 · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N.; McDaniel, P.; Wu, X.; Jha, S.; and Swami, A. 2016 · 2016
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Towards evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.-Y.; Zhang, H.; Sharma, Y.; Yi, J.; and Hsieh, C.-J. 2017 · 2017
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Boosting Adversarial Attacks with Momentum
Dong, Y.; Pang, T.; Su, H.; Zhu, J.; Hu, X.; and Li, J. 2017 · 2017
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Adversarial examples in the physical world
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A.; Carlini, N.; and Wagner, D. 2018 · 2018
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Empirical Study of the Topology and Geometry of Deep Networks
Fawzi, A.; Moosavi-Dezfooli, S.-M.; Frossard, P.; and Soatto, S. 2018 · 2018
Cited alongside, same era.
Imagenette
Howard, J. 2018 · 2018
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With Friends Like These, Who Needs Adversaries?
Jetley, S.; Lord, N.; and Torr, P. 2018 · 2018
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Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Ross, A. S.; and Doshi-Velez, F. 2018 · 2018
Cited alongside, same era.
Ensemble adversarial training: attacks and defenses
Tramèr, F.; Kurakin, A.; Papernot, N.; Goodfellow, I.; Boneh, D.; and McDaniel, P. 2018 · 2018
Cited alongside, same era.
Dataset distillation using neural feature regression
Zhou, Y.; Nezhadarya, E.; and Ba, J. 2022 · 2022
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Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
Buzaglo, G.; Haim, N.; Yehudai, G.; Vardi, G.; Oz, Y.; Nikankin, Y.; and Irani, M. 2023 · 2023
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A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
Chen, Z.; Geng, J.; Zhu, D.; Woisetschlaeger, H.; Li, Q.; Schimmler, S.; Mayer, R.; and Rong, C. 2023 · 2023
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Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
Cui, J.; Wang, R.; Si, S.; and Hsieh, C.-J. 2023 · 2023
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A Survey on Dataset Distillation: Approaches, Applications, and Future Directions
Geng, J.; Chen, Z.; Wang, Y.; Woisetschlaeger, H.; Li, Q.; Schimmler, S.; Mayer, R.; Zhao, Z.; and Rong, C. 2023 · 2023
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Wang, T.; Zhu, J.-Y.; Torralba, A.; and Efros, A. A. 2018 · 2018
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J.; Rosenfield, E.; and Kolter, Z. 2019 · 2019
Cited alongside, same era.
Robustness via curvature regularization, and vice versa
Moosavi-Dezfooli, S.-M.; Fawzi, A.; Uesato, J.; and Frossard, P. 2019 · 2019
Cited alongside, same era.
Square attack: a query-efficient black-box adversarial attack via random search
Andriushchenko, M.; Croce, F.; Flammarion1, N.; and Hein, M. 2020 · 2020
Cited alongside, same era.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F.; and Hein, M. 2020 · 2020
Cited alongside, same era.
Attacks which do not kill training make adversarial learning stronger
Zhang, J.; Xu, X.; Han, B.; Niu, G.; Cui, L.; Sugiyama, M.; and Kankanhalli, M. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Some Intriguing Aspects about Lipschitz Continuity of Neural Networks
Khromov, G.; and Singh, S. P. 2023 · 2023
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Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Liu, H.; Chaudhary, M.; and Wang, H. 2023 · 2023
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Dataset Distillation via the Wasserstein Metric
Liu, H.; Xing, T.; Li, L.; Dalal, V.; He, J.; and Wang, H. 2023 · 2023
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Sachdeva, N.; and McAuley, J. 2023 · 2023
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Dataset Distillation in Large Data Era
Yin, Z.; and Shen, Z. 2023 · 2023
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Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
Yin, Z.; Xing, E.; and Shen, Z. 2023 · 2023
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Dataset condensation with distribution matching
Zhao, B.; and Bilen, H. 2023 · 2023
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Prioritize Alignment in Dataset Distillation
Li, Z.; Guo, Z.; Zhao, W.; Zhang, T.; Cheng, Z.-Q.; Khaki, S.; Zhang, K.; Sajedi, A.; Plataniotis, K. N.; Wang, K.; and You, Y. 2024 · 2024
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Dataset Distillation by Automatic Training Trajectories
Liu, D.; Gu, J.; Cao, H.; Trinitis, C.; and Schulz, M. 2024 · 2024
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Towards trustworthy dataset distillation
Ma, S.; Zhu, F.; Cheng, Z.; and Zhang, X.-Y. 2024 · 2024
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Group Distributionally Robust Dataset Distillation with Risk Minimization
Vahidian, S.; Wang, M.; Gu, J.; Kungurtsev, V.; Jiang, W.; and Chen, Y. 2024 · 2024
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Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation
Xu, Y.; Li, Y.-L.; Cui, K.; Wang, Z.; Lu, C.; Tai, Y.-W.; and Tang, C.-K. 2024 · 2024
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M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Zhang, H.; Li, S.; Wang, P.; and Zeng, S., Dan Ge. 2024 · 2024
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Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
Liu, H.; Singh, A.; Li, Y.; and Wang, H. 2025 · 2025
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