Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

5 December 2014

Jeff Clune

Papers citing "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"

50 / 1,403 papers shown

Title
Robust Deep Learning Ensemble against Deception Wenqi Wei Ling Liu AAML 45 29 0 14 Sep 2020
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports S. Dhaubhadel J. Mohd-Yusof K. Ganguly Gopinath Chennupati S. Thulasidasan ... Georgia D. Tourassi Linda Coyle Lynne Penberthy Benjamin H. McMahon T. Bhattacharya 14 2 0 10 Sep 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 38 157 0 08 Sep 2020
Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model J. Wolff 26 5 0 07 Sep 2020
Problems in AI research and how the SP System may help to solve them J. G. Wolﬀ 6 2 0 02 Sep 2020
Adversarially Robust Neural Architectures Minjing Dong Yanxi Li Yunhe Wang Chang Xu AAML OOD 49 48 0 02 Sep 2020
Inducing Predictive Uncertainty Estimation for Face Recognition Weidi Xie J. Byrne Andrew Zisserman CVBM 24 16 0 01 Sep 2020
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates Lingkai Kong Jimeng Sun Chao Zhang UQCV 61 103 0 24 Aug 2020
Perceptual underwater image enhancement with deep learning and physical priors Long Chen Zheheng Jiang Lei Tong Zhihua Liu Aite Zhao Qianni Zhang Junyu Dong Huiyu Zhou 29 33 0 21 Aug 2020
A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples Julia Lust Alexandru Paul Condurache UQCV AAML AI4CE 29 8 0 21 Aug 2020
Feature Extraction Functions for Neural Logic Rule Learning Shashank Gupta A. Robles-Kelly Mohamed Reda Bouadjenek NAI 38 0 0 14 Aug 2020
Optimizing Information Loss Towards Robust Neural Networks Philip Sperl Konstantin Böttinger AAML 26 3 0 07 Aug 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 34 73 0 07 Aug 2020
Vulnerability Under Adversarial Machine Learning: Bias or Variance? Hossein Aboutalebi M. Shafiee Michelle Karg C. Scharfenberger A. Wong AAML 8 3 0 01 Aug 2020
A General Framework For Detecting Anomalous Inputs to DNN Classifiers Jayaram Raghuram Varun Chandrasekaran S. Jha Suman Banerjee AAML 36 33 0 29 Jul 2020
AI Data poisoning attack: Manipulating game AI of Go Junli Shen Maocai Xia AAML 9 3 0 23 Jul 2020
Inverting the Feature Visualization Process for Feedforward Neural Networks Christian Reinbold Rüdiger Westermann FAtt 11 0 0 21 Jul 2020
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers Yuzhen Ding Nupur Thakur Baoxin Li AAML 35 3 0 20 Jul 2020
DiffRNN: Differential Verification of Recurrent Neural Networks Sara Mohammadinejad Brandon Paulsen Chao Wang Jyotirmoy V. Deshmukh 42 12 0 20 Jul 2020
Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks Nupur Thakur Yuzhen Ding Baoxin Li AAML 11 3 0 20 Jul 2020
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Vladimiro González-Zelaya P. Missier Magdalene Ng Aad van Moorsel 39 17 0 17 Jul 2020
Camera Bias in a Fine Grained Classification Task Philip T. G. Jackson Stephen Bonner Ning Jia Christopher J. Holder Jonathan Stonehouse B. Obara 6 4 0 16 Jul 2020
Certifiably Adversarially Robust Detection of Out-of-Distribution Data Julian Bitterwolf Alexander Meinke Matthias Hein 10 9 0 16 Jul 2020
Nested Learning For Multi-Granular Tasks Raphaël Achddou J. Matias Di Martino Guillermo Sapiro 22 1 0 13 Jul 2020
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation Leonard E. van Dyck W. Gruber 42 3 0 13 Jul 2020
Contrastive Training for Improved Out-of-Distribution Detection Jim Winkens Rudy Bunel Abhijit Guha Roy Robert Stanforth Vivek Natarajan ... Alan Karthikesalingam Simon A. A. Kohl taylan. cemgil S. M. Ali Eslami Olaf Ronneberger OODD 41 236 0 10 Jul 2020
Boundary thickness and robustness in learning models Yaoqing Yang Rekha Khanna Yaodong Yu A. Gholami Kurt Keutzer Joseph E. Gonzalez Kannan Ramchandran Michael W. Mahoney OOD 23 40 0 09 Jul 2020
Detection of Gravitational Waves Using Bayesian Neural Networks Yu-Chiung Lin Jiun-Huei Proty Wu 8 27 0 08 Jul 2020
MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts Nabeel Seedat OOD 14 5 0 08 Jul 2020
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks Doyup Lee Yeongjae Cheon 20 6 0 07 Jul 2020
Confidence-Aware Learning for Deep Neural Networks J. Moon Jihyo Kim Younghak Shin Sangheum Hwang UQCV 38 146 0 03 Jul 2020
Outlier Detection through Null Space Analysis of Neural Networks Matthew Cook A. Zare P. Gader 12 17 0 02 Jul 2020
Are there any óbject detectors' in the hidden layers of CNNs trained to identify objects or scenes? E. Gale Nicholas Martin R. Blything Anh Nguyen J. Bowers 31 14 0 02 Jul 2020
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey S. Silva Peyman Najafirad AAML OOD 31 132 0 01 Jul 2020
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 41 628 0 01 Jul 2020
Generating Adversarial Examples with an Optimized Quality Aminollah Khormali Daehun Nyang David A. Mohaisen AAML 10 1 0 30 Jun 2020
Classification Confidence Estimation with Test-Time Data-Augmentation Yuval Bahat Gregory Shakhnarovich 22 17 0 30 Jun 2020
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches Vahdat Abdelzad Krzysztof Czarnecki Rick Salay 32 1 0 25 Jun 2020
Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders Nina Shvetsova Bart Bakker Irina Fedulova H. Schulz Dmitry V. Dylov 13 82 0 23 Jun 2020
Self-Knowledge Distillation with Progressive Refinement of Targets Kyungyul Kim Byeongmoon Ji Doyoung Yoon Sangheum Hwang ODL 27 178 0 22 Jun 2020
Towards an Adversarially Robust Normalization Approach Muhammad Awais Fahad Shamshad Sung-Ho Bae AAML OOD 52 19 0 19 Jun 2020
Adversarial Attacks for Multi-view Deep Models Xuli Sun Shiliang Sun AAML 9 0 0 19 Jun 2020
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik Pratul P. Srinivasan B. Mildenhall Sara Fridovich-Keil N. Raghavan Utkarsh Singhal R. Ramamoorthi Jonathan T. Barron Ren Ng 67 2,372 0 18 Jun 2020
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs Peijie Chen Chirag Agarwal Anh Totti Nguyen AAML 8 17 0 16 Jun 2020
Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs Christopher Brix T. Noll AAML 25 10 0 16 Jun 2020
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness Mohammadamin Tavakoli Forest Agostinelli Pierre Baldi AAML FAtt 36 39 0 16 Jun 2020
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data Zhun Deng Linjun Zhang Amirata Ghorbani James Zou 47 32 0 15 Jun 2020
Depth Uncertainty in Neural Networks Javier Antorán J. Allingham José Miguel Hernández-Lobato UQCV OOD BDL 46 101 0 15 Jun 2020
The Pitfalls of Simplicity Bias in Neural Networks Harshay Shah Kaustav Tamuly Aditi Raghunathan Prateek Jain Praneeth Netrapalli AAML 18 352 0 13 Jun 2020
Towards Robust Pattern Recognition: A Review Xu-Yao Zhang Cheng-Lin Liu C. Suen OOD HAI 35 103 0 12 Jun 2020