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Deep neural networks (DNNs) are increasingly deployed in different applications to achieve state-of-the-art performance.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 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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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Devnet: A deep event network for multimedia event detection and evidence recounting
Gan, C., Wang, N., Yang, Y., Yeung, D., and Hauptmann, A. G · 2015
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A · 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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Adversarial manipulation of deep representations
Sabour, S., Cao, Y., Faghri, F., and Fleet, D. J · 2016
Cited alongside, same era.
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 2016
Cited alongside, same era.
benefits of depth in neural networks
Telgarsky, M · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., and Torralba, A · 2016
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X., Liu, C., Li, B., Lu, K., and Song, D · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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What do deep networks like to see?
Palacio, S., Folz, J., Hees, J., Raue, F., Borth, D., and Dengel, A · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
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Defending neural backdoors via generative distribution modeling
Qiao, X., Yang, Y., and Li, H · 2019
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Fong, R. C. and Vedaldi, A · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
Cited alongside, same era.
Universal adversarial perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., and Frossard, P · 2017
Cited alongside, same era.
Depth-width tradeoffs in approximating natural functions with neural networks
Safran, I. and Shamir, O · 2017
Cited alongside, same era.
Trojaning attack on neural networks
Liu, Y., Ma, S., Aafer, Y., Lee, W., Zhai, J., Wang, W., and Zhang, X · 2018
Cited alongside, same era.
Characterizing adversarial subspaces using local intrinsic dimensionality
Ma, X., Li, B., Wang, Y., Erfani, S. M., Wijewickrema, S., Schoenebeck, G., Song, D., Houle, M. E., and Bailey, J · 2018
Cited alongside, same era.
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H., and Zhao, B. Y · 2019
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Latent backdoor attacks on deep neural networks
Yao, Y., Li, H., Zheng, H., and Zhao, B. Y · 2019
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Reflection backdoor: A natural backdoor attack on deep neural networks
Liu, Y., Ma, X., Bailey, J., and Lu, F · 2020
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High-frequency component helps explain the generalization of convolutional neural networks
Wang, H., Wu, X., Huang, Z., and Xing, E. P · 2020
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Clean-label backdoor attacks on video recognition models
Zhao, S., Ma, X., Zheng, X., Bailey, J., Chen, J., and Jiang, Y · 2020
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