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Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D.; and Dietterich, T. 2019 · 1903
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CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Yun, S.; Han, D.; Oh, S. J.; Chun, S.; Choe, J.; and Yoo, Y. 2019 · 1905
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MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning
Mai, Z.; Hu, G.; Chen, D.; Shen, F.; and Shen, H. T. 2019 · 1908
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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Hendrycks, D.; Mu, N.; Cubuk, E. D.; Zoph, B.; Gilmer, J.; and Lakshminarayanan, B. 2020 · 1912
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014 · 1958
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Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Kim, J.-H.; Choo, W.; and Song, H. O. 2020 · 2009
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. 2009 · 2009
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Reading Digits in Natural Images with Unsupervised Feature Learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Hinton, G.; Deng, L.; Yu, D.; Dahl, G. E.; Mohamed, A.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T. N.; and Kingsbury, B. 2012 · 2012
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ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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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 · 2013
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Explaining and Harnessing Adversarial Examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Sequence to Sequence Learning with Neural Networks
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014 · 2014
Cited alongside, same era.
Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2015 · 2015
Cited alongside, same era.
Variational dropout and the local reparameterization trick
Kingma, D. P.; Salimans, T.; and Welling, M. 2015 · 2015
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; Berg, A. C.; and Fei-Fei, L. 2015 · 2015
Cited alongside, same era.
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
Chrabaszcz, P.; Loshchilov, I.; and Hutter, F. 2017 · 2017
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Improved Regularization of Convolutional Neural Networks with Cutout
DeVries, T.; and Taylor, G. W. 2017 · 2017
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Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Zagoruyko, S.; and Komodakis, N. 2017 · 2017
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Tishby, N.; and Zaslavsky, N. 2015 · 2015
Cited alongside, same era.
A theoretically grounded application of dropout in recurrent neural networks
Gal, Y.; and Ghahramani, Z. 2016 · 2016
Cited alongside, same era.
Deep learning
Goodfellow, I.; Bengio, Y.; and Courville, A. 2016 · 2016
Cited alongside, same era.
Identity Mappings in Deep Residual Networks
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Zoneout: Regularizing rnns by randomly preserving hidden activations
Krueger, D.; Maharaj, T.; Kramár, J.; Pezeshki, M.; Ballas, N.; Ke, N. R.; Goyal, A.; Bengio, Y.; Courville, A.; and Pal, C. 2016 · 2016
Cited alongside, same era.
Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M.; Fawzi, A.; and Frossard, P. 2016 · 2016
Cited alongside, same era.
A closer look at memorization in deep networks
Arpit, D.; Jastrzebski, S.; Ballas, N.; Krueger, D.; Bengio, E.; Kanwal, M. S.; Maharaj, T.; Fischer, A.; Courville, A.; Bengio, Y.; et al. 2017 · 2017
Cited alongside, same era.
Zhang, H.; Cisse, M.; Dauphin, Y. N.; and Lopez-Paz, D. 2017 · 2017
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Information Dropout: Learning Optimal Representations Through Noisy Computation
Achille, A.; and Soatto, S. 2018 · 2018
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DropBlock: A regularization method for convolutional networks
Ghiasi, G.; Lin, T.; and Le, Q. V. 2018 · 2018
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MixUp as Locally Linear Out-Of-Manifold Regularization
Guo, H.; Mao, Y.; and Zhang, R. 2018 · 2018
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Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Rauber, J.; Brendel, W.; and Bethge, M. 2018 · 2018
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AutoAugment: Learning Augmentation Policies from Data
Cubuk, E. D.; Zoph, B.; Mane, D.; Vasudevan, V.; and Le, Q. V. 2019 · 2019
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Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2019 · 2019
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Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Rony, J.; Hafemann, L. G.; Oliveira, L. S.; Ayed, I. B.; Sabourin, R.; and Granger, E. 2019 · 2019
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Manifold Mixup: Better Representations by Interpolating Hidden States
Verma, V.; Lamb, A.; Beckham, C.; Najafi, A.; Mitliagkas, I.; Lopez-Paz, D.; and Bengio, Y. 2019 · 2019
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