Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks
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Kibok Lee | Jinwoo Shin | Honglak Lee | Kimin Lee | Bo Li | Sukmin Yun | Jinwoo Shin | Honglak Lee | Kibok Lee | Kimin Lee | Sukmin Yun | Bo Li
[1] James Bailey,et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.
[2] Richard Nock,et al. Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] P. Rousseeuw. Least Median of Squares Regression , 1984 .
[4] Chong Wang,et al. Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin , 2015, ICML.
[5] Jonathan Krause,et al. The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition , 2015, ECCV.
[6] Li Fei-Fei,et al. MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels , 2017, ArXiv.
[7] Katrien van Driessen,et al. A Fast Algorithm for the Minimum Covariance Determinant Estimator , 1999, Technometrics.
[8] F. Hampel. A General Qualitative Definition of Robustness , 1971 .
[9] James Bailey,et al. Dimensionality-Driven Learning with Noisy Labels , 2018, ICML.
[10] Tom Minka,et al. Principled Hybrids of Generative and Discriminative Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[11] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[12] Dumitru Erhan,et al. Training Deep Neural Networks on Noisy Labels with Bootstrapping , 2014, ICLR.
[13] 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).
[14] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[15] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[16] Daniel P. W. Ellis,et al. Tandem connectionist feature extraction for conventional HMM systems , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[17] Kevin Gimpel,et al. Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise , 2018, NeurIPS.
[18] Jacob Goldberger,et al. Training deep neural-networks using a noise adaptation layer , 2016, ICLR.
[19] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Bin Yang,et al. Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.
[22] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[24] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[25] Mia Hubert,et al. Fast and robust discriminant analysis , 2004, Comput. Stat. Data Anal..
[26] Jun Zhu,et al. Max-Mahalanobis Linear Discriminant Analysis Networks , 2018, ICML.
[27] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[28] Ata Kabán,et al. Compressed fisher linear discriminant analysis: classification of randomly projected data , 2010, KDD.
[29] Kibok Lee,et al. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks , 2018, NeurIPS.
[30] David Wagner,et al. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods , 2017, AISec@CCS.
[31] T. Bernholt. Robust Estimators are Hard to Compute , 2006 .
[32] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[33] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[34] Samy Bengio,et al. Insights on representational similarity in neural networks with canonical correlation , 2018, NeurIPS.
[35] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[36] P. Rousseeuw,et al. Breakdown Points of Affine Equivariant Estimators of Multivariate Location and Covariance Matrices , 1991 .
[37] Kaiming He,et al. Exploring the Limits of Weakly Supervised Pretraining , 2018, ECCV.
[38] Shai Shalev-Shwartz,et al. Decoupling "when to update" from "how to update" , 2017, NIPS.
[39] Xingrui Yu,et al. Co-teaching: Robust training of deep neural networks with extremely noisy labels , 2018, NeurIPS.