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We present a new method of training energy-based models (EBMs) for anomaly detection that leverages low-dimensional structures within data.
Novelty detection and neural network validation
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar · 2018
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Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak · 2018
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Spherical latent spaces for stable variational autoencoders
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Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada, and Keisuke Imoto · 2019
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Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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Learning non-convergent non-persistent short-run mcmc toward energy-based model
Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu · 2019
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Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio · 2019
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Latent variables on spheres for autoencoders in high dimensions
Improved contrastive divergence training of energy based models
Yilun Du, Shuang Li, B. Joshua Tenenbaum, and Igor Mordatch · 2021
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Learning energy-based models by diffusion recovery likelihood
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma · 2021
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{VAEBM}: A symbiosis between variational autoencoders and energy-based models
Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat · 2021
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Autoencoding under normalization constraints
Sangwoong Yoon, Yung-Kyun Noh, and Frank Park · 2021
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Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
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A simple fix to mahalanobis distance for improving near-ood detection
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Deli Zhao, Jiapeng Zhu, and Bo Zhang · 2019
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Divergence triangle for joint training of generator model, energy-based model, and inferential model
Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu · 2019
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The omniglot challenge: a 3-year progress report
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2019
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Flow contrastive estimation of energy-based models
Ruiqi Gao, Erik Nijkamp, Diederik P Kingma, Zhen Xu, Andrew M Dai, and Ying Nian Wu · 2020
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Joint training of variational auto-encoder and latent energy-based model
Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu · 2020
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Learning latent space energy-based prior model
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu · 2020
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NVAE: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
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The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger · 2021
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Draem - a discriminatively trained reconstruction embedding for surface anomaly detection
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No {mcmc} for me: Amortized sampling for fast and stable training of energy-based models
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, and David Duvenaud · 2021
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Generalized energy based models
Michael Arbel, Liang Zhou, and Arthur Gretton · 2021
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Pseudo-spherical contrastive divergence
Lantao Yu, Jiaming Song, Yang Song, and Stefano Ermon · 2021
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A normalized autoencoder for lhc triggers
Barry M Dillon, Luigi Favaro, Tilman Plehn, Peter Sorrenson, and Michael Krämer · 2022
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MCMC should mix: Learning energy-based model with neural transport latent space MCMC
Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, and Ying Nian Wu · 2022
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Vim: Out-of-distribution with virtual-logit matching
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang · 2022
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Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le · 2022
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Adbench: Anomaly detection benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang, Minqi Jiang, and Yue Zhao · 2022
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A tale of two flows: Cooperative learning of langevin flow and normalizing flow toward energy-based model
Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li · 2022
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Guiding energy-based models via contrastive latent variables
Hankook Lee, Jongheon Jeong, Sejun Park, and Jinwoo Shin · 2023
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Enhancing unsupervised anomaly detection with score-guided network
Zongyuan Huang, Baohua Zhang, Guoqiang Hu, Longyuan Li, Yanyan Xu, and Yaohui Jin · 2023
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Anomaly detection in networks via score-based generative models
Dmitrii Gavrilev and Evgeny Burnaev · 2023
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