Guiding Deep Learning System Testing using Surprise Adequacy

25 August 2018

Papers citing "Guiding Deep Learning System Testing using Surprise Adequacy"

21 / 21 papers shown

Title
Towards Comparable Active Learning Thorben Werner Johannes Burchert Lars Schmidt-Thieme 153 0 0 24 Feb 2025
DANDI: Diffusion as Normative Distribution for Deep Neural Network Input Somin Kim Shin Yoo 117 0 0 05 Feb 2025
Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification Ruben Grewal Paolo Tonella Andrea Stocco 123 12 0 29 Apr 2024
Combinatorial Testing for Deep Learning Systems Lei Ma Fuyuan Zhang Minhui Xue Yue Liu Yang Liu Jianjun Zhao Yadong Wang AAML OffRL 66 73 0 20 Jun 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck Basel Alomair Michael E. Houle James Bailey AAML 120 742 0 08 Jan 2018
DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars Yuchi Tian Kexin Pei Suman Jana Baishakhi Ray AAML 80 1,361 0 28 Aug 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,867 0 20 May 2017
DeepXplore: Automated Whitebox Testing of Deep Learning Systems Kexin Pei Yinzhi Cao Junfeng Yang Suman Jana AAML 114 1,375 0 18 May 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 107 894 0 01 Mar 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 247 945 0 21 Oct 2016
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library Nicolas Papernot Fartash Faghri Nicholas Carlini Ian Goodfellow Reuben Feinman ... David Berthelot P. Hendricks Jonas Rauber Rujun Long Patrick McDaniel AAML 90 516 0 03 Oct 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 551 5,912 0 08 Jul 2016
End to End Learning for Self-Driving Cars Mariusz Bojarski D. Testa Daniel Dworakowski Bernhard Firner B. Flepp ... Urs Muller Jiakai Zhang Xin Zhang Jake Zhao Karol Zieba SSL 102 4,179 0 25 Apr 2016
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 120 3,968 0 24 Nov 2015
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving Chenyi Chen Ari Seff A. Kornhauser Jianxiong Xiao 104 1,766 0 01 May 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,145 0 20 Dec 2014
On Using Very Large Target Vocabulary for Neural Machine Translation Sébastien Jean Kyunghyun Cho Roland Memisevic Yoshua Bengio 159 1,011 0 05 Dec 2014
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 499 43,717 0 17 Sep 2014
Sequence to Sequence Learning with Neural Networks Ilya Sutskever Oriol Vinyals Quoc V. Le AIMat 450 20,606 0 10 Sep 2014
Better Mixing via Deep Representations Yoshua Bengio Grégoire Mesnil Yann N. Dauphin Salah Rifai 93 341 0 18 Jul 2012