MNIST-C: A Robustness Benchmark for Computer Vision

5 June 2019

Justin Gilmer

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

50 / 51 papers shown

Title
WATCH: Adaptive Monitoring for AI Deployments via Weighted-Conformal Martingales Drew Prinster Xing Han Anqi Liu Suchi Saria 45 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 66 0 0 13 Feb 2025
Optimal Subspace Inference for the Laplace Approximation of Bayesian Neural Networks Josua Faller Jörg Martin BDL 75 0 0 04 Feb 2025
CTBENCH: A Library and Benchmark for Certified Training Yuhao Mao Stefan Balauca Martin Vechev OOD 47 5 0 07 Jun 2024
Holistic chemical evaluation reveals pitfalls in reaction prediction models Victor Sabanza Gil Andres M Bran Malte Franke Remi Schlama J. Luterbacher Philippe Schwaller ELM 35 1 0 14 Dec 2023
Neither hype nor gloom do DNNs justice Gaurav Malhotra Christian Tsvetkov B. D. Evans 37 117 0 08 Dec 2023
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization Yongyi Su Xun Xu Kui Jia TTA 84 24 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 Edward Choi 42 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 44 1 0 29 Jun 2023
Structural Restricted Boltzmann Machine for image denoising and classification Arkaitz Bidaurrazaga A. Pérez Roberto Santana AI4CE 20 0 0 16 Jun 2023
Enhancing Multiple Reliability Measures via Nuisance-extended Information Bottleneck Jongheon Jeong Sihyun Yu Hankook Lee Jinwoo Shin AAML 49 0 0 24 Mar 2023
Analyzing Effects of Fake Training Data on the Performance of Deep Learning Systems Pratinav Seth Akshat Bhandari Kumud Lakara 25 0 0 02 Mar 2023
A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks S. Emami Gonzalo Martínez-Munoz 20 6 0 22 Feb 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min Lin Weiwei Liu Shuicheng Yan DiffM 28 210 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 21 7 0 18 Dec 2022
Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines Michael Weiss Paolo Tonella BDL UQCV 30 11 0 14 Dec 2022
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling Yunshi Huang Émilie Chouzenoux Victor Elvira J. Pesquet BDL 34 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 33 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 33 122 0 10 Aug 2022
Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing Michael Weiss A. Gómez Paolo Tonella AAML 18 6 0 21 Jul 2022
Understanding the effect of sparsity on neural networks robustness Lukas Timpl R. Entezari Hanie Sedghi Behnam Neyshabur O. Saukh 51 12 0 22 Jun 2022
ADBench: Anomaly Detection Benchmark Songqiao Han Xiyang Hu Hailiang Huang Mingqi Jiang Yue Zhao OOD 46 296 0 19 Jun 2022
Noisy Learning for Neural ODEs Acts as a Robustness Locus Widening Martin Gonzalez H. Hajri Loic Cantat Mihaly Petreczky 39 1 0 16 Jun 2022
Improving Diversity with Adversarially Learned Transformations for Domain Generalization Tejas Gokhale Rushil Anirudh Jayaraman J. Thiagarajan B. Kailkhura Chitta Baral Yezhou Yang 27 28 0 15 Jun 2022
On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity Vincent Szolnoky Viktor Andersson Balázs Kulcsár Rebecka Jörnsten 45 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 18 50 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 49 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 23 5 0 08 Apr 2022
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL) C. Lee Kibae Lee 44 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 52 29 0 15 Mar 2022
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck Anirban Samaddar Sandeep Madireddy Prasanna Balaprakash Tapabrata Maiti Gustavo de los Campos Ian Fischer 24 1 0 04 Mar 2022
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration Ryan Soklaski Michael Yee Theodoros Tsiligkaridis AAML 22 14 0 24 Feb 2022
OmniPrint: A Configurable Printed Character Synthesizer Haozhe Sun Wei-Wei Tu Isabelle M Guyon SyDa 46 7 0 17 Jan 2022
Tracking the risk of a deployed model and detecting harmful distribution shifts Aleksandr Podkopaev Aaditya Ramdas 34 22 0 12 Oct 2021
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration Cian Eastwood I. Mason Christopher K. I. Williams Bernhard Schölkopf TTA 30 51 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 35 70 0 28 Jun 2021
Exposing Previously Undetectable Faults in Deep Neural Networks Isaac Dunn Hadrien Pouget Daniel Kroening T. Melham AAML 34 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 24 18 0 16 Apr 2021
A Review and Refinement of Surprise Adequacy Michael Weiss Rwiddhi Chakraborty Paolo Tonella AAML AI4TS 19 16 0 10 Mar 2021
Grid Cell Path Integration For Movement-Based Visual Object Recognition Niels Leadholm Marcus Lewis Subutai Ahmad 38 6 0 17 Feb 2021
Robustness Threats of Differential Privacy Nurislam Tursynbek Aleksandr Petiushko Ivan Oseledets AAML 30 13 0 14 Dec 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 51 8 0 03 Nov 2020
Regularizing Towards Permutation Invariance in Recurrent Models Edo Cohen-Karlik Avichai Ben David Amir Globerson OOD 24 16 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 53 191 0 25 Jul 2020
Untapped Potential of Data Augmentation: A Domain Generalization Viewpoint Vihari Piratla Shiv Shankar ViT 16 0 0 09 Jul 2020
Improving robustness against common corruptions by covariate shift adaptation Steffen Schneider E. Rusak L. Eck Oliver Bringmann Wieland Brendel Matthias Bethge VLM 42 463 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 21 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 19 71 0 05 Jul 2019