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Sharpness-Aware Minimization (SAM) is an effective method for improving generalization ability by regularizing loss sharpness.
Simplifying neural nets by discovering flat minima
Hochreiter, S. and Schmidhuber, J · 1994
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Flat minima
Hochreiter, S. and Schmidhuber, J · 1997
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P · 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
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Dziugaite, G. K. and Roy, D. M · 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
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Exploring generalization in deep learning
Neyshabur, B., Bhojanapalli, S., McAllester, D., and Srebro, N · 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
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Averaging weights leads to wider optima and better generalization
Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A. G · 2018
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2018
Cited alongside, same era.
Hilbert-based generative defense for adversarial examples
Bai, Y., Feng, Y., Wang, Y., Dai, T., Xia, S.-T., and Jiang, Y · 2019
Cited alongside, same era.
Entropy-sgd: Biasing gradient descent into wide valleys
Chaudhari, P., Choromanska, A., Soatto, S., LeCun, Y., Baldassi, C., Borgs, C., Chayes, J., Sagun, L., and Zecchina, R · 2019
Cited alongside, same era.
Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
Cited alongside, same era.
Adversarial training for free!
Shafahi, A., Najibi, M., Ghiasi, M. A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T · 2019
Cited alongside, same era.
On the convergence and robustness of adversarial training
Efficient sharpness-aware minimization for improved training of neural networks
Du, J., Yan, H., Feng, J., Zhou, J. T., Zhen, L., Goh, R. S. M., and Tan, V. Y · 2021
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Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks
Kwon, J., Kim, J., Park, H., and Choi, I. K · 2021
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Beta-crown: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network robustness verification, 2021
Wang, S., Zhang, H., Xu, K., Lin, X., Jana, S., Hsieh, C.-J., and Kolter, J. Z · 2021
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To be robust or to be fair: Towards fairness in adversarial training
Xu, H., Liu, X., Li, Y., Jain, A., and Tang, J · 2021
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Splash in a flash: Sharpness-aware minimization for efficient liquid splash simulation
Jetly, V., Ibayashi, H., and Nakano, A · 2022
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Wang, Y., Ma, X., Bailey, J., Yi, J., Zhou, B., and Gu, Q · 2019
Cited alongside, same era.
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. v. d., Yuille, A. L., and He, K · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., and Jordan, M · 2019
Cited alongside, same era.
Sharpness-aware minimization for efficiently improving generalization
Foret, P., Kleiner, A., Mobahi, H., and Neyshabur, B · 2020
Cited alongside, same era.
Understanding adversarial attacks on deep learning based medical image analysis systems
Ma, X., Niu, Y., Gu, L., Wang, Y., Zhao, Y., Bailey, J., and Lu, F · 2020
Cited alongside, same era.
Adversarial weight perturbation helps robust generalization
Wu, D., Xia, S.-T., and Wang, Y · 2020
Cited alongside, same era.
Sharpness-aware minimization improves language model generalization
Bahri, D., Mobahi, H., and Tay, Y · 2021
Cited alongside, same era.
Later among the works it cites.
Fisher sam: Information geometry and sharpness aware minimisation
Kim, M., Li, D., Hu, S. X., and Hospedales, T · 2022
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Towards efficient and scalable sharpness-aware minimization
Liu, Y., Mai, S., Chen, X., Hsieh, C.-J., and You, Y · 2022
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Make sharpness-aware minimization stronger: A sparsified perturbation approach
Mi, P., Shen, L., Ren, T., Zhou, Y., Sun, X., Ji, R., and Tao, D · 2022
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When adversarial training meets vision transformers: Recipes from training to architecture
Mo, Y., Wu, D., Wang, Y., Guo, Y., and Wang, Y · 2022
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Improving sharpness-aware minimization with fisher mask for better generalization on language models
Zhong, Q., Ding, L., Shen, L., Mi, P., Liu, J., Du, B., and Tao, D · 2022
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Generalist: Decoupling natural and robust generalization
Wang, H. and Wang, Y · 2023
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Using z3 for formal modeling and verification of fnn global robustness, 2023
Zhang, Y., Wei, Z., Zhang, X., and Sun, M · 2023
Closest in time.