Revealing the Distributional Vulnerability of Discriminators by Implicit Generators

23 August 2021

Papers citing "Revealing the Distributional Vulnerability of Discriminators by Implicit Generators"

46 / 46 papers shown

Title
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network Haoyang Li Xin Eric Wang Ziwei Zhang Wenwu Zhu OODD OOD 58 102 0 07 Dec 2021
Generalized Out-of-Distribution Detection: A Survey Jingkang Yang Kaiyang Zhou Yixuan Li Ziwei Liu 280 916 0 21 Oct 2021
Adversarial Reciprocal Points Learning for Open Set Recognition Guangyao Chen Peixi Peng Xiangqian Wang Yonghong Tian 90 289 0 01 Mar 2021
Training data-efficient image transformers & distillation through attention Hugo Touvron Matthieu Cord Matthijs Douze Francisco Massa Alexandre Sablayrolles Hervé Jégou ViT 339 6,731 0 23 Dec 2020
Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting LongBing Cao CML 29 49 0 01 Jul 2020
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples David Macêdo T. I. Ren Cleber Zanchettin Adriano Oliveira Teresa B Ludermir OODD 39 23 0 07 Jun 2020
Fixing the train-test resolution discrepancy: FixEfficientNet Hugo Touvron Andrea Vedaldi Matthijs Douze Hervé Jégou AAML 229 110 0 18 Mar 2020
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data Yen-Chang Hsu Yilin Shen Hongxia Jin Z. Kira OODD 67 570 0 26 Feb 2020
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One Will Grathwohl Kuan-Chieh Wang J. Jacobsen David Duvenaud Mohammad Norouzi Kevin Swersky VLM 76 542 0 06 Dec 2019
Input complexity and out-of-distribution detection with likelihood-based generative models Joan Serrà David Álvarez Vicencc Gómez Olga Slizovskaia José F. Núñez Jordi Luque OODD 120 276 0 25 Sep 2019
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift Petra Bevandić Ivan Kreso Marin Orsic Sinisa Segvic 70 81 0 03 Aug 2019
Likelihood Ratios for Out-of-Distribution Detection Jie Jessie Ren Peter J. Liu Emily Fertig Jasper Snoek Ryan Poplin M. DePristo Joshua V. Dillon Balaji Lakshminarayanan OODD 158 720 0 07 Jun 2019
On Variational Bounds of Mutual Information Ben Poole Sherjil Ozair Aaron van den Oord Alexander A. Alemi George Tucker SSL 90 806 0 16 May 2019
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 157 1,475 0 11 Dec 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 159 772 0 12 Nov 2018
Do Deep Generative Models Know What They Don't Know? Eric T. Nalisnick Akihiro Matsukawa Yee Whye Teh Dilan Görür Balaji Lakshminarayanan OOD 53 756 0 22 Oct 2018
Out-of-Distribution Detection using Multiple Semantic Label Representations Gabi Shalev Yossi Adi Joseph Keshet OODD 67 85 0 20 Aug 2018
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Ningning Ma Xiangyu Zhang Haitao Zheng Jian Sun 150 4,970 0 30 Jul 2018
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Kimin Lee Kibok Lee Honglak Lee Jinwoo Shin OODD 149 2,044 0 10 Jul 2018
Glow: Generative Flow with Invertible 1x1 Convolutions Diederik P. Kingma Prafulla Dhariwal BDL DRL 263 3,124 0 09 Jul 2018
Predictive Uncertainty Estimation via Prior Networks A. Malinin Mark Gales UD BDL EDL UQCV PER 183 914 0 28 Feb 2018
MINE: Mutual Information Neural Estimation Mohamed Ishmael Belghazi A. Baratin Sai Rajeswar Sherjil Ozair Yoshua Bengio Aaron Courville R. Devon Hjelm DRL 162 1,277 0 12 Jan 2018
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples Kimin Lee Honglak Lee Kibok Lee Jinwoo Shin OODD 110 881 0 26 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 269 9,743 0 25 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 263 12,029 0 19 Jun 2017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks Shiyu Liang Yixuan Li R. Srikant UQCV OODD 133 2,061 0 08 Jun 2017
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? Alex Kendall Y. Gal BDL OOD UD UQCV PER 326 4,700 0 15 Mar 2017
Regularizing Neural Networks by Penalizing Confident Output Distributions Gabriel Pereyra George Tucker J. Chorowski Lukasz Kaiser Geoffrey E. Hinton NoLa 137 1,137 0 23 Jan 2017
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications Tim Salimans A. Karpathy Xi Chen Diederik P. Kingma 75 939 0 19 Jan 2017
Learning from Simulated and Unsupervised Images through Adversarial Training A. Shrivastava Tomas Pfister Oncel Tuzel J. Susskind Wenda Wang Russ Webb GAN 91 1,800 0 22 Dec 2016
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 129 3,441 0 07 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 709 36,708 0 25 Aug 2016
Multi-way Monte Carlo Method for Linear Systems Tao Wu D. Gleich 18 9 0 15 Aug 2016
Concrete Problems in AI Safety Dario Amodei C. Olah Jacob Steinhardt Paul Christiano John Schulman Dandelion Mané 179 2,378 0 21 Jun 2016
Matching Networks for One Shot Learning Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra VLM 331 7,313 0 13 Jun 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.8K 193,426 0 10 Dec 2015
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop Feng Yu Ari Seff Yinda Zhang Shuran Song Thomas Funkhouser Jianxiong Xiao 67 2,331 0 10 Jun 2015
On Markov chain Monte Carlo methods for tall data Rémi Bardenet Arnaud Doucet Chris Holmes 71 279 0 11 May 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 302 19,580 0 09 Mar 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 225 19,017 0 20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 153 3,270 0 05 Dec 2014
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 395 43,589 0 17 Sep 2014
Microsoft COCO: Common Objects in Context Nayeon Lee Michael Maire Serge J. Belongie Lubomir Bourdev Ross B. Girshick James Hays Pietro Perona Deva Ramanan C. L. Zitnick Piotr Dollár ObjD 365 43,524 0 01 May 2014
Describing Textures in the Wild Mircea Cimpoi Subhransu Maji Iasonas Kokkinos S. Mohamed Andrea Vedaldi 3DV 90 2,661 0 14 Nov 2013
Deep Generative Stochastic Networks Trainable by Backprop Yoshua Bengio Eric Thibodeau-Laufer Guillaume Alain J. Yosinski BDL 127 396 0 05 Jun 2013
Representation Learning: A Review and New Perspectives Yoshua Bengio Aaron Courville Pascal Vincent OOD SSL 218 12,422 0 24 Jun 2012