MNIST-C: A Robustness Benchmark for Computer Vision

5 June 2019

Justin Gilmer

Papers citing "MNIST-C: A Robustness Benchmark for Computer Vision"

41 / 41 papers shown

Title
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales Drew Prinster Xing Han Anqi Liu S. Saria 30 0 0 07 May 2025
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
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Luke E. Richards Jessie Yaros Jasen Babcock Coung Ly Robin Cosbey Timothy Doster Cynthia Matuszek NAI 64 0 0 13 Feb 2025
Holistic chemical evaluation reveals pitfalls in reaction prediction models Victor Sabanza Gil Andres M Bran Malte Franke Remi Schlama J. Luterbacher Philippe Schwaller ELM 25 1 0 14 Dec 2023
Neither hype nor gloom do DNNs justice Gaurav Malhotra Christian Tsvetkov B. D. Evans 21 118 0 08 Dec 2023
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization Yongyi Su Xun Xu K. Jia TTA 70 22 0 26 Sep 2023
VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception Jiyoung Lee Seung Wook Kim Seunghyun Won Joonseok Lee Marzyeh Ghassemi James Thorne Jaeseok Choi O.-Kil Kwon E. Choi 24 1 0 03 Aug 2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World Weiran Lin Keane Lucas Neo Eyal Lujo Bauer Michael K. Reiter Mahmood Sharif OOD AAML 22 1 0 29 Jun 2023
Structural Restricted Boltzmann Machine for image denoising and classification Arkaitz Bidaurrazaga A. Pérez Roberto Santana AI4CE 14 0 0 16 Jun 2023
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck Jongheon Jeong Sihyun Yu Hankook Lee Jinwoo Shin AAML 38 0 0 24 Mar 2023
Analyzing Effects of Fake Training Data on the Performance of Deep Learning Systems Pratinav Seth Akshat Bhandari Kumud Lakara 15 0 0 02 Mar 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min-Bin Lin Weiwei Liu Shuicheng Yan DiffM 18 207 0 09 Feb 2023
Benchmark for Uncertainty & Robustness in Self-Supervised Learning H. Bui Iliana Maifeld-Carucci OOD 19 1 0 23 Dec 2022
Confidence-aware Training of Smoothed Classifiers for Certified Robustness Jongheon Jeong Seojin Kim Jinwoo Shin AAML 19 7 0 18 Dec 2022
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling Yunshi Huang Émilie Chouzenoux Victor Elvira J. Pesquet BDL 14 5 0 03 Oct 2022
Reconstruction-guided attention improves the robustness and shape processing of neural networks Seoyoung Ahn Hossein Adeli G. Zelinsky DiffM AAML 25 1 0 27 Sep 2022
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation Taesik Gong Jongheon Jeong Taewon Kim Yewon Kim Jinwoo Shin Sung-Ju Lee OOD TTA 24 120 0 10 Aug 2022
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing Michael Weiss A. Gómez Paolo Tonella AAML 13 6 0 21 Jul 2022
Understanding the effect of sparsity on neural networks robustness Lukas Timpl R. Entezari Hanie Sedghi Behnam Neyshabur O. Saukh 31 11 0 22 Jun 2022
ADBench: Anomaly Detection Benchmark Songqiao Han Xiyang Hu Hailiang Huang Mingqi Jiang Yue Zhao OOD 32 295 0 19 Jun 2022
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening Martin Gonzalez H. Hajri Loic Cantat M. Petreczky 27 1 0 16 Jun 2022
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity Vincent Szolnoky Viktor Andersson Balázs Kulcsár Rebecka Jörnsten 37 5 0 25 May 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
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
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL) C. Lee Kibae Lee 36 4 0 28 Mar 2022
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness Tejas Gokhale Swaroop Mishra Man Luo Bhavdeep Singh Sachdeva Chitta Baral 42 29 0 15 Mar 2022
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration Ryan Soklaski Michael Yee Theodoros Tsiligkaridis AAML 14 14 0 24 Feb 2022
OmniPrint: A Configurable Printed Character Synthesizer Haozhe Sun Wei-Wei Tu Isabelle M Guyon SyDa 32 7 0 17 Jan 2022
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration Cian Eastwood I. Mason Christopher K. I. Williams Bernhard Schölkopf TTA 16 50 0 12 Jul 2021
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation Chaithanya Kumar Mummadi Robin Hutmacher K. Rambach Evgeny Levinkov Thomas Brox J. H. Metzen TTA OOD 27 69 0 28 Jun 2021
Exposing Previously Undetectable Faults in Deep Neural Networks Isaac Dunn Hadrien Pouget Daniel Kroening T. Melham AAML 23 28 0 01 Jun 2021
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators David Stutz Nandhini Chandramoorthy Matthias Hein Bernt Schiele AAML MQ 20 18 0 16 Apr 2021
A Review and Refinement of Surprise Adequacy Michael Weiss Rwiddhi Chakraborty Paolo Tonella AAML AI4TS 11 16 0 10 Mar 2021
Grid Cell Path Integration For Movement-Based Visual Object Recognition Niels Leadholm Marcus Lewis Subutai Ahmad 31 6 0 17 Feb 2021
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 43 8 0 03 Nov 2020
Regularizing Towards Permutation Invariance in Recurrent Models Edo Cohen-Karlik Avichai Ben David Amir Globerson OOD 11 15 0 25 Oct 2020
Robust and Generalizable Visual Representation Learning via Random Convolutions Zhenlin Xu Deyi Liu Junlin Yang Colin Raffel Marc Niethammer OOD AAML 46 190 0 25 Jul 2020
Improving robustness against common corruptions by covariate shift adaptation Steffen Schneider E. Rusak L. Eck Oliver Bringmann Wieland Brendel Matthias Bethge VLM 31 457 0 30 Jun 2020
A simple way to make neural networks robust against diverse image corruptions E. Rusak Lukas Schott Roland S. Zimmermann Julian Bitterwolf Oliver Bringmann Matthias Bethge Wieland Brendel 19 64 0 16 Jan 2020
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions Yao Qin Nicholas Frosst S. Sabour Colin Raffel G. Cottrell Geoffrey E. Hinton GAN AAML 17 71 0 05 Jul 2019
The Variational Deficiency Bottleneck P. Banerjee Guido Montúfar 11 7 0 27 Oct 2018