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Several density estimation methods have shown to fail to detect out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous data.
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality
Nalisnick, E., Matsukawa, A., Teh, Y. W., and Lakshminarayanan, B · 1906
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Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
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Gradient-based learning applied to document recognition
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Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E · 2002
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Estimation of Non-Normalized Statistical Models by Score Matching
Hyvärinen, A · 2005
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Training restricted Boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A · 2009
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No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Grathwohl, W., Kelly, J., Hashemi, M., Norouzi, M., Swersky, K., and Duvenaud, D · 2010
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Machine Learning, etc: notMNIST dataset, September 2011
Bulatov, Y · 2011
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Reading Digits in Natural Images with Unsupervised Feature Learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Testing the Manifold Hypothesis
Fefferman, C., Mitter, S., and Narayanan, H · 2013
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Variational Inference with Normalizing Flows
Rezende, D. J. and Mohamed, S · 2016
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Engineering safety in machine learning
Varshney, K. R · 2016
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LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2016
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Deep Structured Energy Based Models for Anomaly Detection
Zhai, S., Cheng, Y., Lu, W., and Zhang, Z · 2016
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
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Fashion-MNIST: A Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Zagoruyko, S. and Komodakis, N · 2017
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Conditional Noise-Contrastive Estimation of Unnormalised Models
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model
Nijkamp, E., Hill, M., Zhu, S.-C., and Wu, Y. N · 2019
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Likelihood Ratios for Out-of-Distribution Detection
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., DePristo, M. A., Dillon, J. V., and Lakshminarayanan, B · 2019
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Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Song, Y., Garg, S., Shi, J., and Ermon, S · 2019
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Unbiased Implicit Variational Inference
Titsias, M. K. and Ruiz, F. J. R · 2019
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Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
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Ceylan, C. and Gutmann, M. U · 2018
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Deep Learning for Classical Japanese Literature
Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., and Ha, D · 2018
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Hendrycks, D. and Gimpel, K · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Predictive Uncertainty Estimation via Prior Networks
Malinin, A. and Gales, M · 2018
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Spectral Normalization for Generative Adversarial Networks
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Building robust classifiers through generation of confident out of distribution examples
Sricharan, K. and Srivastava, A · 2018
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WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
Choi, H., Jang, E., and Alemi, A. A · 2019
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Charpentier, B., Zügner, D., and Günnemann, S · 2020
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Implicit Generation and Generalization in Energy-Based Models
Du, Y. and Mordatch, I · 2020
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Flow Contrastive Estimation of Energy-Based Models
Gao, R., Nijkamp, E., Kingma, D. P., Xu, Z., Dai, A. M., and Wu, Y. N · 2020
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Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
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A Compact Convolutional Neural Network for Surface Defect Inspection
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Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2020
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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Liang, S., Li, Y., and Srikant, R · 2020
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Energy-based Out-of-distribution Detection
Liu, W., Wang, X., Owens, J. D., and Li, Y · 2020
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Density of States Estimation for Out-of-Distribution Detection
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Schirrmeister, R. T., Zhou, Y., Ball, T., and Zhang, D · 2020
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Input complexity and out-of-distribution detection with likelihood-based generative models
Serrà, J., Álvarez, D., Gómez, V., Slizovskaia, O., Núñez, J. F., and Luque, J · 2020
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Deep Residual Flow for Out of Distribution Detection
Zisselman, E. and Tamar, A · 2020
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