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[1] Bing Zhang,et al. Semi-supervised learning improves gene expression-based prediction of cancer recurrence , 2011, Bioinform..
[2] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[3] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[4] Tolga Tasdizen,et al. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning , 2016, NIPS.
[5] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] David Elworthy,et al. Does Baum-Welch Re-estimation Help Taggers? , 1994, ANLP.
[7] Cho-Jui Hsieh,et al. Convergence of Adversarial Training in Overparametrized Neural Networks , 2019, NeurIPS.
[8] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[10] Thorsten Joachims,et al. Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.
[11] Saibal Mukhopadhyay,et al. Cascade Adversarial Machine Learning Regularized with a Unified Embedding , 2017, ICLR.
[12] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[13] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[14] Dale Schuurmans,et al. Learning With Adversary , 2015 .
[15] Shin Ishii,et al. Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Aleksander Madry,et al. Robustness May Be at Odds with Accuracy , 2018, ICLR.
[17] Andrew Gordon Wilson,et al. There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average , 2018, ICLR.
[18] Dale Schuurmans,et al. Learning with a Strong Adversary , 2015, ArXiv.
[19] Abhinav Gupta,et al. Non-local Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[21] Luc Van Gool,et al. Temporal Segment Networks: Towards Good Practices for Deep Action Recognition , 2016, ECCV.
[22] Amir Najafi,et al. Robustness to Adversarial Perturbations in Learning from Incomplete Data , 2019, NeurIPS.
[23] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Colin Raffel,et al. Thermometer Encoding: One Hot Way To Resist Adversarial Examples , 2018, ICLR.
[25] Cho-Jui Hsieh,et al. Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network , 2018, ICLR.
[26] Patrick D. McDaniel,et al. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.
[27] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[28] Bin Dong,et al. You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle , 2019, NeurIPS.
[29] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[30] Prateek Mittal,et al. PAC-learning in the presence of evasion adversaries , 2018, NIPS 2018.
[31] Xiaogang Wang,et al. Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[33] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[34] Lorenzo Torresani,et al. Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[35] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[36] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[37] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Enhua Wu,et al. Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Po-Sen Huang,et al. Are Labels Required for Improving Adversarial Robustness? , 2019, NeurIPS.
[40] Ludwig Schmidt,et al. Unlabeled Data Improves Adversarial Robustness , 2019, NeurIPS.
[41] Martin J. Wainwright,et al. High-Dimensional Statistics , 2019 .
[42] Cho-Jui Hsieh,et al. Convergence of Adversarial Training in Overparametrized Networks , 2019, ArXiv.
[43] Kaiming He,et al. Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[44] Bin Dong,et al. You Only Propagate Once: Painless Adversarial Training Using Maximal Principle , 2019 .
[45] Shin Ishii,et al. Distributional Smoothing with Virtual Adversarial Training , 2015, ICLR 2016.
[46] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[47] Hamza Fawzi,et al. Adversarial vulnerability for any classifier , 2018, NeurIPS.
[48] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[49] Michael I. Jordan,et al. Theoretically Principled Trade-off between Robustness and Accuracy , 2019, ICML.
[50] Larry S. Davis,et al. Adversarial Training for Free! , 2019, NeurIPS.
[51] Tapani Raiko,et al. Semi-supervised Learning with Ladder Networks , 2015, NIPS.
[52] Tong Zhang,et al. NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks , 2019, ICML.
[53] Harini Kannan,et al. Adversarial Logit Pairing , 2018, NIPS 2018.
[54] Koby Crammer,et al. New Regularized Algorithms for Transductive Learning , 2009, ECML/PKDD.
[55] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[56] James Bailey,et al. On the Convergence and Robustness of Adversarial Training , 2021, ICML.
[57] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Aleksander Madry,et al. Adversarially Robust Generalization Requires More Data , 2018, NeurIPS.