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The past few years have seen impressive progress in the development of deep generative models capable of producing high-dimensional, complex, and photo-realistic data.
Pattern recognition and machine learning
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Scikit-learn: Machine learning in Python
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The CIFAR-10 dataset
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Importance weighted autoencoders
Y. Burda, R. Grosse, and R. Salakhutdinov · 2015
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A note on the evaluation of generative models
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Deep learning
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TorchVision: PyTorch’s Computer Vision library
T. maintainers and contributors · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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On the quantitative analysis of decoder-based generative models
Y. Wu, Y. Burda, R. Salakhutdinov, and R. Grosse · 2016
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Real-valued (medical) time series generation with recurrent conditional GANs
C. Esteban, S. L. Hyland, and G. Rätsch · 2017
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Deep models under the GAN: information leakage from collaborative deep learning
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
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Do GANs learn the distribution? some theory and empirics
S. Arora, A. Risteski, and Y. Zhang · 2018
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Demystifying MMD GANs
M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton · 2018
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Generative model: Membership attack, generalization and diversity
K. S. Liu, B. Li, and J. Gao · 2018
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Are gans created equal? a large-scale study
M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet · 2018
Cited alongside, same era.
Do deep generative models know what they don’t know?
E. Nalisnick, A. Matsukawa, Y. W. Teh, D. Gorur, and B. Lakshminarayanan · 2018
Cited alongside, same era.
Assessing generative models via precision and recall
M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
Empirical analysis of overfitting and mode drop in gan training
Y. Yazici, C.-S. Foo, S. Winkler, K.-H. Yap, and V. Chandrasekhar · 2020
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On training sample memorization: Lessons from benchmarking generative modeling with a large-scale competition
C.-Y. Bai, H.-T. Lin, C. Raffel, and W. C.-w. Kan · 2021
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A variational perspective on diffusion-based generative models and score matching
C.-W. Huang, J. H. Lim, and A. C. Courville · 2021
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Perfect density models cannot guarantee anomaly detection
C. Le Lan and L. Dinh · 2021
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Maximum likelihood training of score-based diffusion models
Y. Song, C. Durkan, I. Murray, and S. Ermon · 2021
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On memorization in probabilistic deep generative models
G. van den Burg and C. Williams · 2021
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Cited alongside, same era.
An empirical study on evaluation metrics of generative adversarial networks
Q. Xu, G. Huang, Y. Yuan, C. Guo, Y. Sun, F. Wu, and K. Weinberger · 2018
Cited alongside, same era.
Investigating under and overfitting in wasserstein generative adversarial networks
B. Adlam, C. Weill, and A. Kapoor · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Cited alongside, same era.
Improved precision and recall metric for assessing generative models
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila · 2019
Cited alongside, same era.
Detecting overfitting of deep generative networks via latent recovery
R. Webster, J. Rabin, L. Simon, and F. Jurie · 2019
Cited alongside, same era.
Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Later among the works it cites.
Protein sequence design with deep generative models
Z. Wu, K. E. Johnston, F. H. Arnold, and K. K. Yang · 2021
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How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models
A. Alaa, B. Van Breugel, E. S. Saveliev, and M. van der Schaar · 2022
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Probabilistic Machine Learning: An introduction
K. P. Murphy · 2022
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On aliased resizing and surprising subtleties in gan evaluation
G. Parmar, R. Zhang, and J.-Y. Zhu · 2022
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High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
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Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein · 2022
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Extracting Training Data from Diffusion Models
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace · 2023
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Dinov2: Learning robust visual features without supervision
M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, et al · 2023
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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
G. Stein, J. C. Cresswell, R. Hosseinzadeh, Y. Sui, B. L. Ross, V. Villecroze, Z. Liu, A. L. Caterini, J. E. T. Taylor, and G. Loaiza-Ganem · 2023
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