Fetching the paper…
Reading the bibliography…
Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data than in-distribution (ID) data.
L. E. J. Brouwer, “Beweis der invarianz desn-dimensionalen gebiets,” Mathematische Annalen , vol. 71, no. 3, pp. 305–313, 1911
1911
Earlier work this paper cites.
S. M. Ali and S. D. Silvey, “A general class of coefficients of divergence of one distribution from another,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 28, no. 1, pp. 131–142, 1966
1966
Earlier work this paper cites.
J. L. Rodgers and W. A. Nicewander, “Thirteen ways to look at the correlation coefficient,” The American Statistician , vol. 42, no. 1, pp. 59–66, 1988
1988
Earlier work this paper cites.
M. Buckland and F. Gey, “The relationship between recall and precision,” Journal of the American society for information science , vol. 45, no. 1, pp. 12–19, 1994
1994
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
S. Stergiopoulos, Advanced Signal Processing Handbook . CRC Press, 2001
2001
Earlier work this paper cites.
Qing Wang, S. R. Kulkarni, and S. Verdu, “Divergence estimation of continuous distributions based on data-dependent partitions,” IEEE Transactions on Information Theory , vol. 51, no. 9, pp. 3064–3074, 2005
2005
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) . Berlin, Heidelberg: Springer-Verlag, 2006
2006
Earlier work this paper cites.
X. Nguyen, M. J. Wainwright, and M. I. Jordan, “Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization,” in In Advances in Neural Information Processing Systems (NIPS) , 2007
2007
Earlier work this paper cites.
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv. , vol. 41, no. 3, Jul. 2009
2009
Earlier work this paper cites.
H. Hoijtink, I. Klugkist, L. D. Broemeling, R. Jensen, Q. Shen, S. Mukherjee, R. A. Bailey, J. L. Rosenberger, J. D. Leeuw, E. Meijer, B. G. Leroux, A. B. Tsybakov, W. Wefelmeyer, P. C. Consul, F. Famoye, and D. Richards, “Introduction to nonparametric estimation.” 2009
2009
Earlier work this paper cites.
Q. Wang, S. R. Kulkarni, and S. Verdu, “Divergence estimation for multidimensional densities via k k -nearest-neighbor distances,” IEEE Transactions on Information Theory , vol. 55, no. 5, pp. 2392–2405, 2009
2009
Earlier work this paper cites.
A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” Citeseer, Tech. Rep., 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
Earlier work this paper cites.
X. Nguyen, M. J. Wainwright, and M. I. Jordan, “Estimating divergence functionals and the likelihood ratio by convex risk minimization,” IEEE Trans. Inf. Theor. , vol. 56, no. 11, p. 5847–5861, Nov. 2010
2010
Earlier work this paper cites.
L. Xiong, B. Poczos, J. Schneider, A. Connolly, and J. VanderPlas, “Hierarchical Probabilistic Models for Group Anomaly Detection.” Journal of Machine Learning Research - Proceedings Track , vol. 15, pp. 789–797, 2011
2011
Earlier work this paper cites.
L. Xiong, B. Póczos, and J. Schneider, “Group anomaly detection using flexible genre models,” in Proceedings of the 24th International Conference on Neural Information Processing Systems , ser. NIPS’11. Red Hook, NY, USA: Curran Associates Inc., 2011, p. 1071–1079
2011
Earlier work this paper cites.
L. Xiong, B. Póczos, and J. Schneider, “Group anomaly detection using flexible genre models,” in Proceedings of the 24th International Conference on Neural Information Processing Systems , ser. NIPS’11. Red Hook, NY, USA: Curran Associates Inc., 2011, p. 1071–1079
2011
Earlier work this paper cites.
N. Mohd Razali and B. Yap, “Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests,” J. Stat. Model. Analytics , vol. 2, 01 2011
2011
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011
2011
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, Elements of information theory . John Wiley & Sons, 2012
2012
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” vol. 13, no. null, p. 723–773, mar 2012
2012
Earlier work this paper cites.
K. Muandet and B. Schölkopf, “One-class support measure machines for group anomaly detection,” in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence , ser. UAI’13. Arlington, Virginia, USA: AUAI Press, 2013, p. 449–458
2013
Earlier work this paper cites.
M. Giraudo, L. Sacerdote, and R. Sirovich, “Non–parametric estimation of mutual information through the entropy of the linkage,” Entropy , vol. 15, no. 12, p. 5154–5177, Nov 2013
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
M. A. F. Pimentel, D. A. Clifton, C. Lei, and L. Tarassenko, “A review of novelty detection,” Signal Processing , vol. 99, no. 6, pp. 215–249, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. R. Moon and A. O. Hero, “Ensemble estimation of multivariate f f -divergence,” in 2014 IEEE International Symposium on Information Theory , 2014, pp. 356–360
2014
Earlier work this paper cites.
G. Jorge, C. Stephane, and H. R, “Support measure data description for group anomaly detection,” ODDx3 Workshop on Outlier Definition, Detection, and Description at ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2015) , 2015
2015
Earlier work this paper cites.
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, “Human-level concept learning through probabilistic program induction,” Science , vol. 350, no. 6266, pp. 1332–1338, 2015
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of International Conference on Computer Vision (ICCV) , December 2015
2015
Earlier work this paper cites.
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao, “Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,” 2015
2015
Earlier work this paper cites.
J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” Special Lecture on IE , vol. 2, no. 1, 2015
2015
Earlier work this paper cites.
Z. Zhao, K. G. Mehrotra, and C. K. Mohan, “Ensemble Algorithms for Unsupervised Anomaly Detection,” in Current Approaches in Applied Artificial Intelligence , M. Ali, Y. S. Kwon, C.-H. Lee, J. Kim, and Y. Kim, Eds. Springer International Publishing, 2015, pp. 514–525
2015
Earlier work this paper cites.
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves et al. , “Conditional image generation with pixelcnn decoders,” in Advances in neural information processing systems , 2016, pp. 4790–4798
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Los Alamitos, CA, USA: IEEE Computer Society, jun 2016, pp. 770–778
2016
Cited alongside, same era.
S. Nowozin, B. Cseke, and R. Tomioka, “ f f -GAN: Training generative neural samplers using variational divergence minimization,” in Advances in Neural Information Processing Systems , D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds., vol. 29. Curran Associates, Inc., 2016
2016
Cited alongside, same era.
M. D. Hoffman and M. J. Johnson, “ELBO surgery: yet another way to carve up the variational evidence lower bound,” in Workshop in Advances in Approximate Bayesian Inference, NIPS , vol. 1, 2016
A. Grover, M. Dhar, and S. Ermon, “Flow-GAN: Combining maximum likelihood and adversarial learning in generative models,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 , S. A. McIlraith and K. Q. Weinberger, Eds. AAAI Press, 2018, pp. 3069–3076
2018
Later among the works it cites.
A. Kuppa, S. Grzonkowski, M. R. Asghar, and N. Le-Khac, “Finding rats in cats: Detecting stealthy attacks using group anomaly detection,” in 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) , 2019, pp. 442–449
2019
Later among the works it cites.
R. Chalapathy and S. Chawla, “Deep learning for anomaly detection: A survey,” 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
Q. Liu, J. D. Lee, and M. Jordan, “A kernelized stein discrepancy for goodness-of-fit tests,” in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 , ser. ICML’16. JMLR.org, 2016, p. 276–284
2016
Cited alongside, same era.
J. Sneyers and P. Wuille, “FLIF: Free lossless image format based on maniac compression,” in 2016 IEEE International Conference on Image Processing (ICIP) , 2016, pp. 66–70
2016
Cited alongside, same era.
Y. Ye, T. Li, D. Adjeroh, and S. S. Iyengar, “A survey on malware detection using data mining techniques,” ACM Comput. Surv. , vol. 50, no. 3, Jun. 2017
2017
Cited alongside, same era.
L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density estimation using Real NVP,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma, “PixelCNN++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications,” Proceedings of the International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved training of Wasserstein GANs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. Red Hook, NY, USA: Curran Associates Inc., 2017, p. 5769–5779
2017
Cited alongside, same era.
A. Kumar, P. Sattigeri, and A. Balakrishnan, “Variational inference of disentangled latent concepts from unlabeled observations,” in International Conference on Learning Representations , 2017
2017
Cited alongside, same era.
D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” in Proceedings of the International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
E. Nalisnick, A. Matsukawa, Y. W. Teh, D. Gorur, and B. Lakshminarayanan, “Do deep generative models know what they don’t know?” ICLR , 2019
2019
Later among the works it cites.
E. Nalisnick, A. Matsukawa, Y. W. Teh, and B. Lakshminarayanan, “Detecting out-of-distribution inputs to deep generative models using typicality,” 4th workshop on Bayesian Deep Learning (NeurIPS 2019) , 2019
2019
Later among the works it cites.
R. T. Q. Chen, J. Behrmann, D. Duvenaud, and J. Jacobsen, “Residual flows for invertible generative modeling,” in Advances in Neural Information Processing Systems , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
P. Izmailov, P. Kirichenko, M. Finzi, and A. G. Wilson, “Semi-supervised learning with normalizing flows,” 2019
2019
Later among the works it cites.
E. Sabeti and A. Hostmadsen, “Data discovery and anomaly detection using atypicality for real-valued data,” Entropy , vol. 21, no. 3, p. 219, 2019
2019
Later among the works it cites.
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. A. DePristo, J. V. Dillon, and B. Lakshminarayanan, “Likelihood ratios for out-of-distribution detection,” 2019
2019
Later among the works it cites.
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normalizing flows for probabilistic modeling and inference,” 2019
2019
Later among the works it cites.
A. Gambardella, A. G. Baydin, and P. H. S. Torr, “Transflow learning: Repurposing flow models without retraining,” 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, and O. Bachem, “Challenging common assumptions in the unsupervised learning of disentangled representations,” in Proceedings of the 36th International Conference on Machine Learning , 2019
2019
Later among the works it cites.
D. Hendrycks, M. Mazeika, and T. G. Dietterich, “Deep anomaly detection with outlier exposure,” International Conference on Learning Representations (ICLR) , 2019
2019
Later among the works it cites.
Y. Bulatov, “notMNIST,” http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html , 2011, accessed October 4, 2019
2019
Later among the works it cites.
N. . D. Challenge, https://www.aicrowd.com/challenges/neurips-2019-disentanglement-challenge
2019
Later among the works it cites.
J. Bian, X. Hui, S. Sun, X. Zhao, and M. Tan, “A novel and efficient cvae-gan-based approach with informative manifold for semi-supervised anomaly detection,” IEEE Access , vol. 7, pp. 88 903–88 916, 2019
2019
Later among the works it cites.
A. Atanov, A. Volokhova, A. Ashukha, I. Sosnovik, and D. Vetrov, “Semi-conditional normalizing flows for semi-supervised learning,” 2019
2019
Later among the works it cites.
S. P. Mishra and P. Kumari, “Analysis of Techniques for Credit Card Fraud Detection: A Data Mining Perspective,” in New Paradigm in Decision Science and Management , S. Patnaik, A. W. H. Ip, M. Tavana, and V. Jain, Eds. Singapore: Springer Singapore, 2020, pp. 89–98
2020
Closest in time.
P. Kirichenko, P. Izmailov, and A. G. Wilson, “Why normalizing flows fail to detect out-of-distribution data,” ICML workshop on Invertible Neural Networks and Normalizing Flows, 2020 (NeurIPS 2020) , 2020
2020
Closest in time.
J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J. F. Núñez, and J. Luque, “Input complexity and out-of-distribution detection with likelihood-based generative models,” in International Conference on Learning Representations , 2020
2020
Closest in time.
R. T. Schirrmeister, Y. Zhou, T. Ball, and D. Zhang, “Understanding Anomaly Detection with DeepInvertible Networks through Hierarchies of Distributions and Features ,” in Advances in Neural Information Processing Systems 33 . Curran Associates, Inc., 2020
2020
Closest in time.
2020
Closest in time.
N. Dionelis, M. Yaghoobi, and S. A. Tsaftaris, “Boundary of distribution support generator (BDSG): Sample generation on the boundary,” in 2020 IEEE International Conference on Image Processing (ICIP) . IEEE, oct 2020. [Online]. Available: https://doi.org/10.1109%2Ficip40778.2020.9191341
2020
Closest in time.
G. Pang, C. Shen, L. Cao, and A. V. D. Hengel, “Deep learning for anomaly detection: A review,” ACM Comput. Surv. , vol. 54, no. 2, Mar. 2021
2021
Closest in time.
W. Morningstar, C. Ham, A. Gallagher, B. Lakshminarayanan, A. Alemi, and J. Dillon, “Density of States Estimation for Out of Distribution Detection,” in Proceedings of The 24th International Conference on Artificial Intelligence and Statistics , vol. 130. PMLR, 2021, pp. 3232–3240
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
J. D. D. Havtorn, J. Frellsen, S. Hauberg, and L. Maaløe, “Hierarchical vaes know what they don’t know,” in Proceedings of the 38th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, M. Meila and T. Zhang, Eds., vol. 139. PMLR, 18–24 Jul 2021, pp. 4117–4128
2021
Closest in time.
A. Sinha, K. Ayush, J. Song, B. Uzkent, H. Jin, and S. Ermon, “Negative data augmentation,” International Conference on Learning Representations (ICLR) , 2021
2021
Closest in time.
D. Jiang, S. Sun, and Y. Yu, “Revisiting flow generative models for out-of-distribution detection,” in International Conference on Learning Representations , 2022
2022
Closest in time.
G. Osada, T. Tsubasa, B. Ahsan, and T. Nishide, “Out-of-distribution detection with reconstruction error and typicality-based penalty,” 2022
2022
Closest in time.
Z. Liu, Z. Cen, V. Isenbaev, W. Liu, Z. S. Wu, B. Li, and D. Zhao, “Constrained variational policy optimization for safe reinforcement learning,” in The 5th Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) , 2022
2022
Closest in time.