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Deep generative models have been demonstrated as state-of-the-art density estimators.
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
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Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality
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A Mathematical Theory of Communication
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Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position
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The likelihood ratio, Wald, and Lagrange multiplier tests: An expository note
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Gradient-based learning applied to document recognition
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Learning methods for generic object recognition with invariance to pose and lighting
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NVAE: A Deep Hierarchical Variational Autoencoder
Vahdat, A. and Kautz, J · 2007
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A · 2009
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Aspects of multivariate statistical theory , volume 197
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Rectified linear units improve restricted boltzmann machines
Nair, V. and Hinton, G. E · 2010
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notMNIST dataset, September 2011
Bulatov, Y · 2011
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Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
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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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Representation learning: A review and new perspectives
Bengio, Y., Courville, A. C., and Vincent, P · 2013
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Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
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Semi-Supervised Learning with Deep Generative Models
Kingma, D. P., Rezende, D. J., Mohamed, S., and Welling, M · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J. L · 2015
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 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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Uncertainty in the Variational Information Bottleneck
Alemi, A. A., Fischer, I., and Dillon, J. V · 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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Inference Suboptimality in Variational Autoencoders
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Variational Inference with Normalizing Flows
Rezende, D. J. and Mohamed, S · 2015
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Importance Weighted Autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R. R · 2016
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Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
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Variational Graph Auto-Encoders
Kipf, T. N. and Welling, M · 2016
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Auxiliary deep generative models
Maaløe, L., Sønderby, C. K., Sønderby, S. K., and Winther, O · 2016
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Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Salimans, T. and Kingma, D. P · 2016
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Cremer, C., Li, X., and Duvenaud, D · 2018
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Learning Confidence for Out-of-Distribution Detection in Neural Networks
DeVries, T. and Taylor, G. W · 2018
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Glow: Generative Flow with Invertible 1×1 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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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2018
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Refit your encoder when new data comes by
Mattei, P.-A. and Frellsen, J · 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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Avoiding latent variable collapse with generative skip models
Dieng, A. B., Kim, Y., Rush, A. M., and Blei, D. M · 2019
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Deep anomaly detection with outlier exposure
Hendrycks, D., Mazeika, M., and Dietterich, T. G · 2019
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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P · 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., Dillon, J., and Lakshminarayanan, B · 2019
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Practical Lossless Compression With Latent Variables Using Bits Back Coding
Townsend, J., Bird, T., and Barber, D · 2019
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Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features
Schirrmeister, R., 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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Likelihood Regret: An Out-of-Distribution Detection Score for Variational Auto-Encoder
Xiao, Z., Yan, Q., and Amit, Y · 2020
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