Test Selection for Deep Learning Systems

30 April 2019

Papers citing "Test Selection for Deep Learning Systems"

26 / 26 papers shown

Title
MetaSel: A Test Selection Approach for Fine-tuned DNN Models Amin Abbasishahkoo Mahboubeh Dadkhah Lionel C. Briand Dayi Lin 49 0 0 21 Mar 2025
FAST: Boosting Uncertainty-based Test Prioritization Methods for Neural Networks via Feature Selection Jialuo Chen Jingyi Wang Xiyue Zhang Youcheng Sun Marta Kwiatkowska Jiming Chen Peng Cheng 11 0 0 13 Sep 2024
LeCov: Multi-level Testing Criteria for Large Language Models Xuan Xie Jiayang Song Yuheng Huang Da Song Fuyuan Zhang Felix Juefei-Xu Lei Ma ELM 29 0 0 20 Aug 2024
A3Rank: Augmentation Alignment Analysis for Prioritizing Overconfident Failing Samples for Deep Learning Models Zhengyuan Wei Haipeng Wang Qili Zhou William Chan 34 0 0 19 Jul 2024
DeepKnowledge: Generalisation-Driven Deep Learning Testing S. Missaoui Simos Gerasimou Nikolaos Matragkas 27 0 0 25 Mar 2024
GIST: Generated Inputs Sets Transferability in Deep Learning Florian Tambon Foutse Khomh G. Antoniol AAML 34 1 0 01 Nov 2023
Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations Salah Ghamizi Maxime Cordy Yuejun Guo Mike Papadakis And Yves Le Traon 16 1 0 11 Sep 2023
Evaluating the Robustness of Test Selection Methods for Deep Neural Networks Qiang Hu Yuejun Guo Xiaofei Xie Maxime Cordy Wei Ma Mike Papadakis Yves Le Traon NoLa OOD 22 3 0 29 Jul 2023
CertPri: Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space Haibin Zheng Jinyin Chen Haibo Jin AAML 13 7 0 18 Jul 2023
Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks Ferhat Ozgur Catak Marc Brittain Murat Kuzlu Christine Serres UQCV 15 1 0 04 May 2023
DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks Zohreh Aghababaeyan Manel Abdellatif Mahboubeh Dadkhah Lionel C. Briand AAML 28 15 0 08 Mar 2023
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities Wei Ma Shangqing Liu Mengjie Zhao Xiaofei Xie Wenhan Wang Q. Hu Jiexin Zhang Yang Liu 19 16 0 20 Dec 2022
An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty Zhijie Wang Yuheng Huang L. Ma Haruki Yokoyama Susumu Tokumoto Kazuki Munakata 24 4 0 13 Dec 2022
Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation Qiang Hu Yuejun Guo Xiaofei Xie Maxime Cordy Lei Ma Mike Papadakis Yves Le Traon AAML 12 17 0 22 Jul 2022
Guiding the retraining of convolutional neural networks against adversarial inputs Francisco Durán Silverio Martínez-Fernández Michael Felderer Xavier Franch AAML 30 1 0 08 Jul 2022
Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study) Michael Weiss Paolo Tonella AAML 8 49 0 02 May 2022
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment Qiang Hu Yuejun Guo Maxime Cordy Xiaofei Xie Wei Ma Mike Papadakis Yves Le Traon MQ 36 1 0 08 Apr 2022
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing Qiang Hu Yuejun Guo Maxime Cordy Xiaofei Xie Mike Papadakis Yves Le Traon 21 5 0 08 Apr 2022
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks Xiaofei Xie Tianlin Li Jian-Xun Wang L. Ma Qing-Wu Guo Felix Juefei Xu Yang Liu AAML 11 50 0 24 Mar 2022
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models Yuejun Guo Qiang Hu Maxime Cordy Mike Papadakis Yves Le Traon VLM OOD 14 4 0 05 Dec 2021
A Review and Refinement of Surprise Adequacy Michael Weiss Rwiddhi Chakraborty Paolo Tonella AAML AI4TS 13 16 0 10 Mar 2021
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty Xiyue Zhang Xiaofei Xie Lei Ma Xiaoning Du Q. Hu Yang Liu Jianjun Zhao Meng Sun AAML 8 76 0 24 Apr 2020
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 19 739 0 19 Jun 2019
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 281 5,835 0 08 Jul 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015