Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

6 December 2019

Papers citing "Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One"

48 / 48 papers shown

Title
Energy-based Preference Optimization for Test-time Adaptation Yewon Han Seoyun Yang Taesup Kim TTA 139 0 0 26 May 2025
Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art Youssef Shoeb Azarm Nowzad Hanno Gottschalk UQCV 132 2 0 04 Mar 2025
Your contrastive learning problem is secretly a distribution alignment problem Zihao Chen Chi-Heng Lin Ran Liu Jingyun Xiao Eva L. Dyer 88 1 0 27 Feb 2025
Benchmarking Predictive Coding Networks -- Made Simple Luca Pinchetti Chang Qi Oleh Lokshyn Gaspard Olivers Cornelius Emde ... Simon Frieder Bayar I. Menzat Rafal Bogacz Thomas Lukasiewicz Tommaso Salvatori 140 6 0 17 Feb 2025
GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation Danny Wang Ruihong Qiu Guangdong Bai Zi Huang 259 2 0 09 Feb 2025
Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations Xin-Chao Xu Yi Qin Lu Mi Hao Wang Xuelong Li 94 11 0 03 Jan 2025
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes Yuanpeng Tu Yuxi Li Boshen Zhang Liang Liu Jing Zhang Yun Wang C. Zhao 114 3 0 03 Jan 2025
Pretrained Reversible Generation as Unsupervised Visual Representation Learning Rongkun Xue Jinouwen Zhang Yazhe Niu Dazhong Shen Bingqi Ma Yu Liu Jing Yang 100 0 0 29 Nov 2024
Artificial Kuramoto Oscillatory Neurons Takeru Miyato Sindy Löwe Andreas Geiger Max Welling AI4CE 127 7 0 17 Oct 2024
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics Omar Chehab Anna Korba Austin Stromme Adrien Vacher 87 3 0 13 Oct 2024
Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness Hefei Mei Minjing Dong Chang Xu AAML 94 0 0 16 Aug 2024
Uncertainty for Active Learning on Graphs Dominik Fuchsgruber Tom Wollschlager Bertrand Charpentier Antonio Oroz Stephan Günnemann 87 11 0 02 May 2024
Continual Adversarial Defense Qian Wang Yaoyao Liu Hefei Ling Yingwei Li Qihao Liu Ping Li AAML 75 4 0 15 Dec 2023
Maximizing Discrimination Capability of Knowledge Distillation with Energy Function Seonghak Kim Gyeongdo Ham Suin Lee Donggon Jang Daeshik Kim 104 4 0 24 Nov 2023
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 129 3,803 0 12 Jul 2019
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers Hadi Salman Greg Yang Jungshian Li Pengchuan Zhang Huan Zhang Ilya P. Razenshteyn Sébastien Bubeck AAML 57 544 0 09 Jun 2019
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality Eric T. Nalisnick Akihiro Matsukawa Yee Whye Teh Balaji Lakshminarayanan OODD 37 86 0 07 Jun 2019
Residual Flows for Invertible Generative Modeling Ricky T. Q. Chen Jens Behrmann David Duvenaud J. Jacobsen BDL TPM DRL 40 375 0 06 Jun 2019
Image Synthesis with a Single (Robust) Classifier Shibani Santurkar Dimitris Tsipras Brandon Tran Andrew Ilyas Logan Engstrom Aleksander Madry AAML 21 34 0 06 Jun 2019
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model Erik Nijkamp Mitch Hill Song-Chun Zhu Ying Nian Wu 48 210 0 22 Apr 2019
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models Erik Nijkamp Mitch Hill Tian Han Song-Chun Zhu Ying Nian Wu 32 154 0 29 Mar 2019
Invertible Residual Networks Jens Behrmann Will Grathwohl Ricky T. Q. Chen David Duvenaud J. Jacobsen UQCV TPM 65 621 0 02 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 39 753 0 22 Oct 2018
Glow: Generative Flow with Invertible 1x1 Convolutions Diederik P. Kingma Prafulla Dhariwal BDL DRL 179 3,110 0 09 Jul 2018
Adversarial Distillation of Bayesian Neural Network Posteriors Kuan-Chieh Wang Paul Vicol James Lucas Li Gu Roger C. Grosse R. Zemel UQCV GAN AAML BDL 31 56 0 27 Jun 2018
Conditional Noise-Contrastive Estimation of Unnormalised Models Ciwan Ceylan Michael U. Gutmann 38 42 0 10 Jun 2018
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection Yunfu Song Zhijian Ou DiffM 35 30 0 01 Jun 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 36 369 0 23 May 2018
Are Generative Classifiers More Robust to Adversarial Attacks? Yingzhen Li John Bradshaw Yash Sharma AAML 66 78 0 19 Feb 2018
Spectral Normalization for Generative Adversarial Networks Takeru Miyato Toshiki Kataoka Masanori Koyama Yuichi Yoshida ODL 121 4,421 0 16 Feb 2018
First-order Adversarial Vulnerability of Neural Networks and Input Dimension Carl-Johann Simon-Gabriel Yann Ollivier Léon Bottou Bernhard Schölkopf David Lopez-Paz AAML 44 48 0 05 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 147 3,171 0 01 Feb 2018
A Note on the Inception Score Shane T. Barratt Rishi Sharma EGVM 55 688 0 06 Jan 2018
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel Jonas Rauber Matthias Bethge AAML 55 1,335 0 12 Dec 2017
Wasserstein Introspective Neural Networks Kwonjoon Lee Weijian Xu Fan Fan Zhuowen Tu 46 57 0 24 Nov 2017
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 89 787 0 30 Oct 2017
Foolbox: A Python toolbox to benchmark the robustness of machine learning models Jonas Rauber Wieland Brendel Matthias Bethge AAML 38 283 0 13 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 188 11,962 0 19 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 154 5,774 0 14 Jun 2017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks Shiyu Liang Yixuan Li R. Srikant UQCV OODD 86 2,046 0 08 Jun 2017
PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications Tim Salimans A. Karpathy Xi Chen Diederik P. Kingma 36 933 0 19 Jan 2017
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks Dan Hendrycks Kevin Gimpel UQCV 86 3,420 0 07 Oct 2016
Improved Techniques for Training GANs Tim Salimans Ian Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford Xi Chen GAN 323 8,999 0 10 Jun 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 217 7,951 0 23 May 2016
A Theory of Generative ConvNet Jianwen Xie Yang Lu Song-Chun Zhu Ying Nian Wu DiffM GAN 65 318 0 10 Feb 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 415 9,233 0 06 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 493 149,474 0 22 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 126 18,922 0 20 Dec 2014