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He et al.
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Learning from imbalanced data
He, H. and Garcia, E. A · 2008
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., jia Li, L., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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ImageNet classification with deep convolutional neural networks
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Analyzing the performance of multilayer neural networks for object recognition
Agrawal, P., Girshick, R., and Malik, J · 2014
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Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Microsoft COCO: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., and Dollar, P · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A · 2014
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Training convolutional networks with noisy labels
Sukhbaatar, S., Bruna, J., Paluri, M., Bourdev, L., and Fergus, R · 2014
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Intriguing properties of neural networks, 2014
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Posterior calibration and exploratory analysis for natural language processing models
Nguyen, K. and O’Connor, B · 2015
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Learning deep representation for imbalanced classification
Huang, C., Li, Y., Change Loy, C., and Tang, X · 2016
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What makes ImageNet good for transfer learning?
Huh, M., Agrawal, P., and Efros, A. A · 2016
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SGDR: stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods, 2017
Carlini, N. and Wagner, D · 2017
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Learning from untrusted data
Charikar, M., Steinhardt, J., and Valiant, G · 2017
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A downsampled variant of ImageNet as an alternative to the CIFAR datasets
Chrabaszcz, P., Loshchilov, I., and Hutter, F · 2017
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Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2017
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Evaluating and understanding the robustness of adversarial logit pairing
Engstrom, L., Ilyas, A., and Athalye, A · 2018
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Generalisation in humans and deep neural networks
Geirhos, R., Temme, C. R. M., Rauber, J., Schütt, H. H., Bethge, M., and Wichmann, F. A · 2018
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Co-teaching: robust training deep neural networks with extremely noisy labels
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M · 2018
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Rethinking ImageNet pre-training
He, K., Girshick, R., and Dollar, P · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Hendrycks, D., Mazeika, M., Wilson, D., and Gimpel, K · 2018
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Adversarial logit pairing
Kannan, H., Kurakin, A., and Goodfellow, I · 2018
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Mask R-CNN
He, K., Gkioxari, G., Dollar, P., and Girshick, R · 2017
Cited alongside, same era.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2017
Cited alongside, same era.
On detecting adversarial perturbations, 2017
Metzen, J. H., Genewein, T., Fischer, V., and Bischoff, B · 2017
Cited alongside, same era.
Making deep neural networks robust to label noise: a loss correction approach
Patrini, G., Rozza, A., Menon, A., Nock, R., and Qu, L · 2017
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Learning multiple visual domains with residual adapters
Rebuffi, S.-A., Bilen, H., and Vedaldi, A · 2017
Cited alongside, same era.
Open category detection with PAC guarantees
Liu, S., Garrepalli, R., Dietterich, T., Fern, A., and Hendrycks, D · 2018
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Dimensionality-driven learning with noisy labels
Ma, X., Wang, Y., Houle, M. E., Zhou, S., Erfani, S. M., Xia, S.-T., Wijewickrema, S., and Bailey, J · 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
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Logit pairing methods can fool gradient-based attacks
Mosbach, M., Andriushchenko, M., Trost, T., Hein, M., and Klakow, D · 2018
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Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
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The inaturalist species classification and detection dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., and Belongie, S · 2018
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Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., van der Maaten, L., Yuille, A., and He, K · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2019
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