Five Points to Check when Comparing Visual Perception in Humans and Machines

20 April 2020

Wieland Brendel

Papers citing "Five Points to Check when Comparing Visual Perception in Humans and Machines"

42 / 42 papers shown

Title
Do Neural Networks for Segmentation Understand Insideness? Kimberly M Villalobos Vilim Štih Amineh Ahmadinejad Shobhita Sundaram Jamell Dozier Andrew Francl Frederico Azevedo Tomotake Sasaki Xavier Boix 72 8 0 25 Jan 2022
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency Robert Geirhos Kristof Meding Felix Wichmann 60 123 0 30 Jun 2020
A neural network walks into a lab: towards using deep nets as models for human behavior Wei-Ying Ma B. Peters HAI AI4CE 71 55 0 02 May 2020
Going in circles is the way forward: the role of recurrence in visual inference R. S. V. Bergen N. Kriegeskorte 52 82 0 26 Mar 2020
Controversial stimuli: pitting neural networks against each other as models of human recognition Tal Golan Prashant C. Raju N. Kriegeskorte AAML 49 39 0 21 Nov 2019
On the Variance of the Adaptive Learning Rate and Beyond Liyuan Liu Haoming Jiang Pengcheng He Weizhu Chen Xiaodong Liu Jianfeng Gao Jiawei Han ODL 284 1,903 0 08 Aug 2019
Challenge of Spatial Cognition for Deep Learning Xi Zhang Xiaolin Wu Jun Du 30 4 0 30 Jul 2019
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming Claudio Michaelis Benjamin Mitzkus Robert Geirhos E. Rusak Oliver Bringmann Alexander S. Ecker Matthias Bethge Wieland Brendel 3DPC 95 445 0 17 Jul 2019
Probing Neural Network Comprehension of Natural Language Arguments Timothy Niven Hung-Yu kao AAML 88 454 0 17 Jul 2019
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan Quoc V. Le 3DV MedIm 139 18,115 0 28 May 2019
Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study Chihye Han Wonjun Yoon Gihyun Kwon S. Nam Dae-Shik Kim AAML 53 5 0 07 May 2019
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet Wieland Brendel Matthias Bethge SSL FAtt 84 561 0 20 Mar 2019
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure Been Kim Emily Reif Martin Wattenberg Samy Bengio Michael C. Mozer 71 30 0 04 Mar 2019
Adaptive Gradient Methods with Dynamic Bound of Learning Rate Liangchen Luo Yuanhao Xiong Yan Liu Xu Sun ODL 74 602 0 26 Feb 2019
Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images S. Srivastava Guy Ben-Yosef Xavier Boix AAML 50 27 0 08 Feb 2019
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference R. Thomas McCoy Ellie Pavlick Tal Linzen 131 1,237 0 04 Feb 2019
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness Robert Geirhos Patricia Rubisch Claudio Michaelis Matthias Bethge Felix Wichmann Wieland Brendel 100 2,668 0 29 Nov 2018
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? David Barrett Ari S. Morcos Jakob H. Macke AI4CE 54 110 0 31 Oct 2018
Humans can decipher adversarial images Zhenglong Zhou C. Firestone AAML 34 122 0 11 Sep 2018
Generalisation in humans and deep neural networks Robert Geirhos Carlos R. Medina Temme Jonas Rauber Heiko H. Schutt Matthias Bethge Felix Wichmann OOD 107 607 0 27 Aug 2018
Measuring abstract reasoning in neural networks David Barrett Felix Hill Adam Santoro Ari S. Morcos Timothy Lillicrap OOD 69 362 0 11 Jul 2018
Testing Deep Neural Networks Youcheng Sun Xiaowei Huang Daniel Kroening James Sharp Matthew Hill Rob Ashmore AAML 54 218 0 10 Mar 2018
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans Gamaleldin F. Elsayed Shreya Shankar Brian Cheung Nicolas Papernot Alexey Kurakin Ian Goodfellow Jascha Narain Sohl-Dickstein AAML 67 262 0 22 Feb 2018
How intelligent are convolutional neural networks? Zhennan Yan Xiangmin Zhou 44 11 0 18 Sep 2017
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 63 283 0 13 Jul 2017
Do Deep Neural Networks Suffer from Crowding? Anna Volokitin Gemma Roig T. Poggio 30 34 0 26 Jun 2017
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study Samuel Ritter David Barrett Adam Santoro M. Botvinick 73 196 0 26 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 299 5,827 0 14 Jun 2017
A simple neural network module for relational reasoning Adam Santoro David Raposo David Barrett Mateusz Malinowski Razvan Pascanu Peter W. Battaglia Timothy Lillicrap GNN NAI 177 1,614 0 05 Jun 2017
Adapting Deep Network Features to Capture Psychological Representations Joshua C. Peterson Joshua T. Abbott Thomas Griffiths 44 66 0 06 Aug 2016
25 years of CNNs: Can we compare to human abstraction capabilities? Sebastian Stabinger A. Rodríguez-Sánchez J. Piater 58 56 0 28 Jul 2016
How Deep is the Feature Analysis underlying Rapid Visual Categorization? S. Eberhardt Jonah Cader Thomas Serre 58 53 0 03 Jun 2016
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen George Papandreou Iasonas Kokkinos Kevin Patrick Murphy Alan Yuille SSeg 245 18,232 0 02 Jun 2016
Fully Convolutional Networks for Semantic Segmentation Evan Shelhamer Jonathan Long Trevor Darrell VOS SSeg 738 37,846 0 20 May 2016
A Comparative Evaluation of Approximate Probabilistic Simulation and Deep Neural Networks as Accounts of Human Physical Scene Understanding Renqiao Zhang Jiajun Wu Chengkai Zhang William T. Freeman J. Tenenbaum 52 49 0 04 May 2016
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex Q. Liao T. Poggio 242 257 0 13 Apr 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 193,878 0 10 Dec 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.8K 150,039 0 22 Dec 2014
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture David Eigen Rob Fergus VLM MDE 207 2,679 0 18 Nov 2014
Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet Matthias Kümmerer Lucas Theis Matthias Bethge FAtt 97 407 0 04 Nov 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.6K 100,348 0 04 Sep 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 270 14,918 1 21 Dec 2013