Open Questions in Testing of Learned Computer Vision Functions for Automated Driving
暂无分享,去创建一个
Matthias Woehrle | Christian Heinzemann | Christoph Gladisch | M. Woehrle | C. Gladisch | Christian Heinzemann
[1] Pushmeet Kohli,et al. Verification of Non-Linear Specifications for Neural Networks , 2019, ICLR.
[2] Daniel Cremers,et al. What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? , 2018, International Journal of Computer Vision.
[3] 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).
[4] Xiaolin Hu,et al. UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision , 2016, 2018 International Conference on 3D Vision (3DV).
[5] Christian Müller,et al. Toward a Methodology for Training with Synthetic Data on the Example of Pedestrian Detection in a Frame-by-Frame Semantic Segmentation Task , 2018, 2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS).
[6] Philip Koopman,et al. How Many Operational Design Domains, Objects, and Events? , 2019, SafeAI@AAAI.
[7] Christopher Ré. Software 2.0 and Snorkel: Beyond Hand-Labeled Data , 2018, KDD.
[8] Sanjit A. Seshia,et al. Formal Specification for Deep Neural Networks , 2018, ATVA.
[9] Philip Koopman,et al. Putting Image Manipulations in Context: Robustness Testing for Safe Perception , 2018, 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).
[10] Mykel J. Kochenderfer,et al. Algorithms for Verifying Deep Neural Networks , 2019, Found. Trends Optim..
[11] Thomas G. Dietterich,et al. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , 2018, ICLR.
[12] Timon Gehr,et al. An abstract domain for certifying neural networks , 2019, Proc. ACM Program. Lang..
[13] Sarfraz Khurshid,et al. DeepRoad: GAN-Based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[14] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[15] Junfeng Yang,et al. DeepXplore: Automated Whitebox Testing of Deep Learning Systems , 2017, SOSP.
[16] Baowen Xu,et al. Testing and validating machine learning classifiers by metamorphic testing , 2011, J. Syst. Softw..
[17] Foutse Khomh,et al. On Testing Machine Learning Programs , 2018, J. Syst. Softw..
[18] Andreas Geiger,et al. Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[19] Daniel Kroening,et al. Concolic Testing for Deep Neural Networks , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[20] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[21] Mark Harman,et al. The Oracle Problem in Software Testing: A Survey , 2015, IEEE Transactions on Software Engineering.
[22] Oliver Zendel,et al. WildDash - Creating Hazard-Aware Benchmarks , 2018, ECCV.
[23] Oliver Zendel,et al. CV-HAZOP: Introducing Test Data Validation for Computer Vision , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[24] Sebastian Ramos,et al. The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Benoît Frénay,et al. A comprehensive introduction to label noise , 2014, ESANN.
[26] Philip Koopman,et al. Toward a Framework for Highly Automated Vehicle Safety Validation , 2018 .
[27] Andreas Geiger,et al. Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes , 2017, International Journal of Computer Vision.
[28] Bernt Schiele,et al. Not Using the Car to See the Sidewalk — Quantifying and Controlling the Effects of Context in Classification and Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Matthew Johnson-Roberson,et al. Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation , 2018, IEEE Robotics and Automation Letters.
[30] Koushik Sen,et al. CUTE: a concolic unit testing engine for C , 2005, ESEC/FSE-13.
[31] Markus Borg,et al. Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry , 2018, Journal of Automotive Software Engineering.