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Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data.
Novelty detection and neural network validation
Bishop, C. M · 1994
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Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C · 2003
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Mnist handwritten digit database
LeCun, Y., Cortes, C., and Burges, C · 2010
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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. Y · 2011
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Generative adversarial networks, 2014
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Anomaly detection using autoencoders with nonlinear dimensionality reduction
Sakurada, M. and Yairi, T · 2014
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Learning discriminative reconstructions for unsupervised outlier removal
Xia, Y., Cao, X., Wen, F., Hua, G., and Sun, J · 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2016
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Artificial intelligence in medicine
Hamet, P. and Tremblay, J · 2017
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2017
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Unsupervised anomaly detection with generative adversarial networks to guide marker discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G · 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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Anomaly detection with robust deep autoencoders
Zhou, C. and Paffenroth, R. C · 2017
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Waic, but why? generative ensembles for robust anomaly detection
Choi, H., Jang, E., and Alemi, A. A · 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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Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance
Denouden, T., Salay, R., Czarnecki, K., Abdelzad, V., Phan, B., and Vernekar, S · 2018
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
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Anomaly detection with generative adversarial networks for multivariate time series
Li, D., Chen, D., Goh, J., and Ng, S.-k · 2018
Why normalizing flows fail to detect out-of-distribution data
Kirichenko, P., Izmailov, P., and Wilson, A. G · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Tack, J., Mo, S., Jeong, J., and Shin, J · 2020
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Xiao, Z., Yan, Q., and Amit, Y · 2020
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Likelihood-free out-of-distribution detection with invertible generative models
Ahmadian, A. and Lindsten, F · 2021
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Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Zong, B., Song, Q., Min, M. R., Cheng, W., Lumezanu, C., Cho, D., and Chen, H · 2018
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Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Biva: A very deep hierarchy of latent variables for generative modeling
Maaløe, L., Fraccaro, M., Liévin, V., and Winther, O · 2019
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Detecting out-of-distribution inputs to deep generative models using a test for typicality
Nalisnick, E. T., Matsukawa, A., Teh, Y. W., and Lakshminarayanan, B · 2019
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On the importance of gradients for detecting distributional shifts in the wild
Huang, R., Geng, A., and Li, Y · 2021
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Sdedit: Guided image synthesis and editing with stochastic differential equations
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2021
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Density of states estimation for out of distribution detection
Morningstar, W., Ham, C., Gallagher, A., Lakshminarayanan, B., Alemi, A., and Dillon, J · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Ssd: A unified framework for self-supervised outlier detection
Sehwag, V., Chiang, M., and Mittal, P · 2021
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Do we really need to learn representations from in-domain data for outlier detection?
Xiao, Z., Yan, Q., and Amit, Y · 2021
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Model-agnostic out-of-distribution detection using combined statistical tests
Bergamin, F., Mattei, P.-A., Havtorn, J. D., Senetaire, H., Schmutz, H., Maaløe, L., Hauberg, S., and Frellsen, J · 2022
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Denoising diffusion models for out-of-distribution detection
Graham, M. S., Pinaya, W. H., Tudosiu, P.-D., Nachev, P., Ourselin, S., and Cardoso, M. J · 2022
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Pseudo numerical methods for diffusion models on manifolds
Liu, L., Ren, Y., Lin, Z., and Zhao, Z · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
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Vim: Out-of-distribution with virtual-logit matching
Wang, H., Li, Z., Feng, L., and Zhang, W · 2022
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Learning fast samplers for diffusion models by differentiating through sample quality
Watson, D., Chan, W., Ho, J., and Norouzi, M · 2022
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Simmim: A simple framework for masked image modeling
Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., and Hu, H · 2022
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Diffusion models: A comprehensive survey of methods and applications
Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Shao, Y., Zhang, W., Cui, B., and Yang, M.-H · 2022
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