On-manifold Adversarial Data Augmentation Improves Uncertainty
Calibration

On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

16 December 2019

William H. Beluch

Michael Pfeiffer

Papers citing "On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration"

10 / 10 papers shown

Title
Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin AAML 22 21 0 15 Jul 2023
A Review of Uncertainty Estimation and its Application in Medical Imaging K. Zou Zhihao Chen Xuedong Yuan Xiaojing Shen Meng Wang H. Fu UQCV 43 85 0 16 Feb 2023
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks Sérgio A. Balanya Juan Maroñas Daniel Ramos OOD 35 13 0 31 Jul 2022
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing Kanil Patel William H. Beluch K. Rambach Michael Pfeiffer B. Yang UQCV 27 9 0 27 Sep 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 32 1,109 0 07 Jul 2021
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra Kanil Patel William H. Beluch K. Rambach Adriana-Eliza Cozma Michael Pfeiffer Bin Yang EDL UQCV 16 5 0 01 Jun 2021
On Calibration of Mixup Training for Deep Neural Networks Juan Maroñas D. Ramos-Castro Roberto Paredes Palacios UQCV 25 6 0 22 Mar 2020
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 186 272 0 03 Dec 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 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