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Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training.
Classification Accuracy Score for Conditional Generative Models
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Minimally distorted adversarial examples with a fast adaptive boundary attack
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An Alternative Surrogate Loss for PGD-based Adversarial Testing
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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
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GANSpace: Discovering Interpretable GAN Controls
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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., Song, D., Steinhardt, J., and Gilmer, J · 2006
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Learning to generate noise for robustness against multiple perturbations
Madaan, D., Shin, J., and Hwang, S. J · 2006
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80 million tiny images: a large dataset for non-parametric object and scene recognition
Torralba, A., Fergus, R., and Freeman, W. T · 2008
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Gowal, S., Qin, C., Uesato, J., Mann, T., and Kohli, P · 2010
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Learnable boundary guided adversarial training
Cui, J., Liu, S., Wang, L., and Jia, J · 2011
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 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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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
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SGDR: stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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Ensemble methods as a defense to adversarial perturbations against deep neural networks
Strauss, T., Hanselmann, M., Junginger, A., and Ulmer, H · 2017
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Ensemble Adversarial Training: Attacks and Defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P · 2017
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
Robustness to adversarial perturbations in learning from incomplete data
Najafi, A., Maeda, S.-i., Koyama, M., and Miyato, T · 2019
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Improving adversarial robustness via promoting ensemble diversity
Pang, T., Xu, K., Du, C., Chen, N., and Zhu, J · 2019
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Adversarial Robustness through Local Linearization
Qin, C., Martens, J., Gowal, S., Krishnan, D., Dvijotham, K., Fawzi, A., De, S., Stanforth, R., and Kohli, P · 2019
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Are labels required for improving adversarial robustness?
Uesato, J., Alayrac, J.-B., Huang, P.-S., Stanforth, R., Fawzi, A., and Kohli, P · 2019
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Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., van der Maaten, L., Yuille, A., and He, K · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
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Synthesizing robust adversarial examples
Athalye, A. and Sutskever, I · 2018
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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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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Boosting Adversarial Attacks with Momentum
Dong, Y., Liao, F., Pang, T., Su, H., Zhu, J., Hu, X., and Li, J · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., and Wilson, A. G · 2018
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Strength in numbers: Trading-off robustness and computation via adversarially-trained ensembles
Grefenstette, E., Stanforth, R., O’Donoghue, B., Uesato, J., Swirszcz, G., and Kohli, P · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2018
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Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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Adversarially Robust Generalization Just Requires More Unlabeled Data
Zhai, R., Cai, T., He, D., Dan, C., He, K., Hopcroft, J., and Wang, L · 2019
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Theoretically Principled Trade-off between Robustness and Accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 2019
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Square Attack: a query-efficient black-box adversarial attack via random search
Andriushchenko, M., Croce, F., Flammarion, N., and Hein, M · 2020
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Robustbench: a standardized adversarial robustness benchmark
Croce, F., Andriushchenko, M., Sehwag, V., Flammarion, N., Chiang, M., Mittal, P., and Hein, M · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2020
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Self-Adaptive Training: beyond Empirical Risk Minimization
Huang, L., Zhang, C., and Zhang, H · 2020
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Boosting Adversarial Training with Hypersphere Embedding
Pang, T., Yang, X., Dong, Y., Xu, K., Su, H., and Zhu, J · 2020
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Controlling generative models with continuous factors of variations
Plumerault, A., Borgne, H. L., and Hudelot, C · 2020
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Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, J. Z · 2020
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Adversarial weight perturbation helps robust generalization
Wu, D., Xia, S.-t., and Wang, Y · 2020
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Robust overfitting may be mitigated by properly learned smoothening
Chen, T., Zhang, Z., Liu, S., Chang, S., and Wang, Z · 2021
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Very deep vaes generalize autoregressive models and can outperform them on images
Child, R · 2021
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Improving robustness using generated data
Gowal, S., Rebuffi, S.-A., Olivia Wiles, F. S., Calian, D. A., and Mann, T · 2021
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2021
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Dall-e: Creating images from text, 2021
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Data augmentation can improve robustness
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Learning perturbation sets for robust machine learning
Wong, E. and Kolter, J. Z · 2021
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