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Detecting Overfitting via Adversarial Examples

Neural Information Processing Systems (NeurIPS), 2019

6 March 2019

Papers citing "Detecting Overfitting via Adversarial Examples"

22 / 22 papers shown

Title
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems Tri Minh Triet Pham Karthikeyan Premkumar Mohamed Naili Jinqiu Yang AI4TS 204 0 0 11 Oct 2024
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting Hao Li Gopi Krishnan Rajbahadur Dayi Lin Cor-Paul Bezemer Zhen Ming Jiang Jiang 193 54 0 18 Jan 2024
Limitations of Data-Driven Spectral Reconstruction -- An Optics-Aware AnalysisIEEE Transactions on Computational Imaging (TCI), 2024 Qiang Fu Matheus Souza E. Choi Suhyun Shin Seung-Hwan Baek Wolfgang Heidrich 270 2 0 08 Jan 2024
On Synthetic Data for Back Translation Jiahao Xu Yubin Ruan Wei Bi Guoping Huang Shuming Shi Lihui Chen Lemao Liu 105 14 0 20 Oct 2023
DSRM: Boost Textual Adversarial Training with Distribution Shift Risk MinimizationAnnual Meeting of the Association for Computational Linguistics (ACL), 2023 Songyang Gao Jiajun Sun Yan Liu Xiao Wang Qi Zhang Zhongyu Wei Jin Ma Yingchun Shan OOD 156 9 0 27 Jun 2023
Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Space Hossein Rezaei Mohammad Sabokrou 182 5 0 30 May 2023
Measuring Overfitting in Convolutional Neural Networks using Adversarial Perturbations and Label NoiseIEEE Symposium Series on Computational Intelligence (IEEE SSCI), 2022 Svetlana Pavlitskaya Joël Oswald J. Marius Zöllner NoLa AAML 91 7 0 27 Sep 2022
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary ClassificationInternational Conference on Learning Representations (ICLR), 2022 Takashi Ishida Ikko Yamane Nontawat Charoenphakdee Gang Niu Masashi Sugiyama BDL UQCV 195 21 0 01 Feb 2022
ML4ML: Automated Invariance Testing for Machine Learning ModelsInternational Conference on Artificial Intelligence Testing (ICAIT), 2021 Zukang Liao Pengfei Zhang Min Chen VLM 143 3 0 27 Sep 2021
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT CompressionConference on Empirical Methods in Natural Language Processing (EMNLP), 2021 Canwen Xu Wangchunshu Zhou Tao Ge Kelvin J. Xu Julian McAuley Furu Wei 212 46 0 07 Sep 2021
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event SamplingIEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2021 Mengdi Xu Peide Huang Fengpei Li Jiacheng Zhu Xuewei Qi K. Oguchi Zhiyuan Huang Henry Lam Ding Zhao 172 4 0 19 Jun 2021
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology Alex Foote Amina Asif A. Azam Tim Marshall-Cox Nasir M. Rajpoot F. Minhas AAML MedIm 125 13 0 14 Jun 2021
RDA: Robust Domain Adaptation via Fourier Adversarial AttackingIEEE International Conference on Computer Vision (ICCV), 2021 Jiaxing Huang Dayan Guan Aoran Xiao Shijian Lu AAML 259 82 0 05 Jun 2021
The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks Karim Huesmann Luis Garcia Rodriguez Lars Linsen Benjamin Risse 107 4 0 13 Apr 2021
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial RobustnessProceedings of the IEEE (Proc. IEEE), 2020 Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 328 50 0 19 Oct 2020
PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards Masoud Hashemi Ali Fathi AAML 281 37 0 24 Aug 2020
Robustness and Overfitting Behavior of Implicit Background Models Shirley Liu Charles Lehman Ghassan AlRegib VLM 93 2 0 21 Aug 2020
Tangent Space Sensitivity and Distribution of Linear Regions in ReLU Networks Balint Daroczy AAML 91 0 0 11 Jun 2020
Generating Semantically Valid Adversarial Questions for TableQA Yi Zhu Yiwei Zhou Menglin Xia AAML 213 6 0 26 May 2020
PanNuke Dataset Extension, Insights and Baselines Jevgenij Gamper Navid Alemi Koohbanani Ksenija Benes S. Graham Mostafa Jahanifar S. Khurram A. Azam K. Hewitt Nasir M. Rajpoot 683 214 0 24 Mar 2020
Do We Need Zero Training Loss After Achieving Zero Training Error?International Conference on Machine Learning (ICML), 2020 Takashi Ishida Ikko Yamane Tomoya Sakai Gang Niu Masashi Sugiyama AI4CE 171 154 0 20 Feb 2020
Machine Learning Testing: Survey, Landscapes and HorizonsIEEE Transactions on Software Engineering (TSE), 2019 Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 248 815 0 19 Jun 2019