Fetching the paper…
Reading the bibliography…
Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification.
Casting curved shadows on curved surfaces
Lance Williams · 1978
Earlier work this paper cites.
The rendering equation
James T Kajiya · 1986
Earlier work this paper cites.
Efficient algorithms for local and global accessibility shading
Gavin Miller · 1994
Earlier work this paper cites.
A practical analytic model for daylight
Arcot J Preetham, Peter Shirley, and Brian Smits · 1999
Earlier work this paper cites.
An efficient representation for irradiance environment maps
Ravi Ramamoorthi and Pat Hanrahan · 2001
Earlier work this paper cites.
Lambertian reflectance and linear subspaces
Ronen Basri and David W Jacobs · 2003
Earlier work this paper cites.
Spherical harmonic lighting: The gritty details
Robin Green · 2003
Earlier work this paper cites.
Local, deformable precomputed radiance transfer
Peter-Pike Sloan, Ben Luna, and John Snyder · 2005
Earlier work this paper cites.
Real-time rendering
Tomas Akenine-Moller, Eric Haines, and Naty Hoffman · 2008
Earlier work this paper cites.
Efficient spherical harmonics lighting with the preetham skylight model
Ralf Habel, Bogdan Mustata, and Michael Wimmer · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
OpenGL Programming Guide: The Official Guide to Learning OpenGL, Versions 3.0 and 3.1
Dave Shreiner and The Khronos OpenGL ARB Working Group · 2009
Earlier work this paper cites.
Legendre and related functions
TM Dunster · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
OpenDR: An approximate differentiable renderer
Matthew M Loper and Michael J Black · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
ShapeNet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Render for CNN: Viewpoint estimation in images using CNNs trained with rendered 3d model views
Hao Su, Charles R Qi, Yangyan Li, and Leonidas J Guibas · 2015
Earlier work this paper cites.
Synthesizing training images for boosting human 3d pose estimation
Wenzheng Chen, Huan Wang, Yangyan Li, Hao Su, Zhenhua Wang, Changhe Tu, Dani Lischinski, Daniel Cohen-Or, and Baoquan Chen · 2016
Earlier work this paper cites.
Attend, infer, repeat: Fast scene understanding with generative models
SM Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Geoffrey E Hinton, et al · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
Cited alongside, same era.
3d simulation for robot arm control with deep q-learning
Stephen James and Edward Johns · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Cited alongside, same era.
One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Sakurai Kouichi · 2017
Later among the works it cites.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick McDaniel · 2017
Later among the works it cites.
Learning from synthetic humans
Gül Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J Black, Ivan Laptev, and Cordelia Schmid · 2017
Later among the works it cites.
Model-driven simulations for computer vision
VSR Veeravasarapu, Constantin Rothkopf, and Ramesh Visvanathan · 2017
Later among the works it cites.
Neural scene de-rendering
Jiajun Wu, Joshua B Tenenbaum, and Pushmeet Kohli · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deepfool: a simple and accurate method to fool deep neural networks
Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
Cited alongside, same era.
How useful is photo-realistic rendering for visual learning?
Yair Movshovitz-Attias, Takeo Kanade, and Yaser Sheikh · 2016
Cited alongside, same era.
Physically based rendering: From theory to implementation
Matt Pharr, Wenzel Jakob, and Greg Humphreys · 2016
Cited alongside, same era.
Adversarial diversity and hard positive generation
Andras Rozsa, Ethan M Rudd, and Terrance E Boult · 2016
Cited alongside, same era.
Cad2rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2016
Cited alongside, same era.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Synthesizing robust adversarial examples, 2017
Anish Athalye, Logan Engstrom, Andrew Ilyas, and Kevin Kwok · 2017
Cited alongside, same era.
Xiaohui Zeng, Chenxi Liu, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi Keung Tang, and Alan L Yuille · 2017
Later among the works it cites.
Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal S. Mian · 2018
Closest in time.
Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
Closest in time.
Robust Physical-World Attacks on Deep Learning Visual Classification
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
Closest in time.
Unsupervised training for 3d morphable model regression
Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T. Freeman · 2018
Closest in time.
Motivating the rules of the game for adversarial example research
Justin Gilmer, Ryan P Adams, Ian Goodfellow, David Andersen, and George E Dahl · 2018
Closest in time.
Ian Goodfellow · 2018
Closest in time.
Benchmarking neural network robustness to common corruptions and surface variations
Dan Hendrycks and Thomas G Dietterich · 2018
Closest in time.
Geometric robustness of deep networks: analysis and improvement
Can Kanbak, Seyed Mohsen Moosavi Dezfooli, and Pascal Frossard · 2018
Closest in time.
Neural 3d mesh renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Closest in time.
Paparazzi: Surface editing by way of multi-view image processing
Hsueh-Ti Derek Liu, Michael Tao, and Alec Jacobson · 2018
Closest in time.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Closest in time.
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Shin Ishii, and Masanori Koyama · 2018
Closest in time.
Training augmentation with adversarial examples for robust speech recognition
Sining Sun, Ching-Feng Yeh, Mari Ostendorf, Mei-Yuh Hwang, and Lei Xie · 2018
Closest in time.
Training deep networks with synthetic data: Bridging the reality gap by domain randomization
Jonathan Tremblay, Aayush Prakash, David Acuna, Mark Brophy, Varun Jampani, Cem Anil, Thang To, Eric Cameracci, Shaad Boochoon, and Stan Birchfield · 2018
Closest in time.