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In recent years novel architecture components for image classification have been developed, starting with attention and patches used in transformers.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
Adversarial patch
Brown, T. B., Mané, D., Roy, A., Abadi, M., and Gilmer, J · 2017
Earlier work this paper cites.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Lavan: Localized and visible adversarial noise
Karmon, D., Zoran, D., and Goldberg, Y · 2018
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Unlabeled data improves adversarial robustness
Carmon, Y., Raghunathan, A., Schmidt, L., Duchi, J. C., and Liang, P. S · 2019
Earlier work this paper cites.
Sparse and imperceivable adversarial attacks
Croce, F. and Hein, M · 2019
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Transfer of adversarial robustness between perturbation types
Kang, D., Sun, Y., Brown, T., Hendrycks, D., and Steinhardt, J · 2019
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
Earlier work this paper cites.
Image synthesis with a single (robust) classifier
Santurkar, S., Tsipras, D., Tran, B., Ilyas, A., Engstrom, L., and Madry, A · 2019
Cited alongside, same era.
Adversarial training and robustness for multiple perturbations
Tramèr, F. and Boneh, D · 2019
Cited alongside, same era.
Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
Cited alongside, same era.
Pytorch image models
Wightman, R · 2019
Cited alongside, same era.
Adversarial framing for image and video classification
Zajac, M., Zołna, K., Rostamzadeh, N., and Pinheiro, P. O · 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.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
Later among the works it cites.
Xcit: Cross-covariance image transformers
El-Nouby, A., Touvron, H., Caron, M., Bojanowski, P., Douze, M., Joulin, A., Laptev, I., Neverova, N., Synnaeve, G., Verbeek, J., et al · 2021
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Exploring the limits of out-of-distribution detection
Fort, S., Ren, J., and Lakshminarayanan, B · 2021
Later among the works it cites.
Are vision transformers robust to patch perturbations?
Gu, J., Tresp, V., and Qin, Y · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
Later among the works it cites.
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Croce, F. and Hein, M · 2020
Cited alongside, same era.
Uncovering the limits of adversarial training against norm-bounded adversarial examples
Gowal, S., Qin, C., Uesato, J., Mann, T., and Kohli, P · 2020
Cited alongside, same era.
Do adversarially robust imagenet models transfer better?
Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., and Madry, A · 2020
Cited alongside, same era.
Fast is better than free: Revisiting adversarial training
Wong, E., Rice, L., and Kolter, J. Z · 2020
Cited alongside, same era.
Xie, C., Tan, M., Gong, B., Yuille, A., and Le, Q. V · 2020
Cited alongside, same era.
Patchattack: A black-box texture-based attack with reinforcement learning
Yang, C., Kortylewski, A., Xie, C., Cao, Y., and Yuille, A · 2020
Cited alongside, same era.
Data augmentation can improve robustness
Rebuffi, S.-A., Gowal, S., Calian, D. A., Stimberg, F., Wiles, O., and Mann, T · 2021
Later among the works it cites.
On the adversarial robustness of vision transformers
Shao, R., Shi, Z., Yi, J., Chen, P.-Y., and Hsieh, C.-J · 2021
Later among the works it cites.
Sparse-rs: a versatile framework for query-efficient sparse black-box adversarial attacks
Croce, F., Andriushchenko, M., Singh, N. D., Flammarion, N., and Hein, M · 2022
Closest in time.
Adversarially robust vision transformers
Debenedetti, E · 2022
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Patch-fool: Are vision transformers always robust against adversarial perturbations?
Fu, Y., Zhang, S., Wu, S., Wan, C., and Lin, Y · 2022
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A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
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Give me your attention: Dot-product attention considered harmful for adversarial patch robustness
Lovisotto, G., Finnie, N., Munoz, M., Mummadi, C. K., and Metzen, J. H · 2022
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How do vision transformers work?
Park, N. and Kim, S · 2022
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Vision transformers are robust learners
Paul, S. and Chen, P.-Y · 2022
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Patches are all you need?, 2022
Trockman, A. and Kolter, J. Z · 2022
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