Uncertainty-Guided Testing and Robustness Enhancement for Deep Learning Systems
暂无分享,去创建一个
[1] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[2] Ananthram Swami,et al. Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks , 2015, 2016 IEEE Symposium on Security and Privacy (SP).
[3] 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).
[4] Zhitao Gong,et al. Adversarial and Clean Data Are Not Twins , 2017, aiDM@SIGMOD.
[5] Elmar Haussmann,et al. Training Data Distribution Search with Ensemble Active Learning , 2019 .
[6] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Haijun Wang,et al. DiffChaser: Detecting Disagreements for Deep Neural Networks , 2019, IJCAI.
[8] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[9] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[10] Lei Ma,et al. DeepHunter: a coverage-guided fuzz testing framework for deep neural networks , 2019, ISSTA.
[11] Jingyi Wang,et al. Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing , 2018, 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE).
[12] Shin Yoo,et al. Guiding Deep Learning System Testing Using Surprise Adequacy , 2018, 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE).
[13] Ioannis Mitliagkas,et al. Manifold Mixup: Better Representations by Interpolating Hidden States , 2018, ICML.
[14] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[15] Yang Liu,et al. Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty , 2020, 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE).
[16] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[17] K. Makarychev. Perturbations , Optimization , and Statistics , 2017 .
[18] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[19] Yanjun Qi,et al. Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks , 2017, NDSS.
[20] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[21] James A. Storer,et al. Deflecting Adversarial Attacks with Pixel Deflection , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[22] Junfeng Yang,et al. DeepXplore: Automated Whitebox Testing of Deep Learning Systems , 2017, SOSP.
[23] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[24] Ian Goodfellow,et al. TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing , 2018, ICML.
[25] Suman Jana,et al. DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).
[26] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[27] Lei Ma,et al. DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[28] Qiang Chen,et al. Network In Network , 2013, ICLR.
[29] Yarin Gal,et al. Uncertainty in Deep Learning , 2016 .
[30] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).