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The diffusion probabilistic generative models are widely used to generate high-quality data.
Weak convergence
Van Der Vaart, A. W., Wellner, J. A., van der Vaart, A. W., and Wellner, J. A. (1996) · 1996
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The nature of statistical learning theory
Vapnik, V. (1999) · 1999
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On the optimality of conditional expectation as a bregman predictor
Banerjee, A., Guo, X., and Wang, H. (2005) · 2005
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G. (2009) · 2009
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Tweedie’s formula and selection bias
Efron, B. (2011) · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Auto-encoding variational { \{ Bayes } \}
Kingma, D. P. and Welling, M. (2013) · 2013
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Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y. (2014) · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Lecture notes for statistics 311/electrical engineering 377
Duchi, J. (2016) · 2016
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Probability-1
Shiryaev, A. N. (2016) · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (gans)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y. (2017) · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
Cited alongside, same era.
Information-theoretic analysis of generalization capability of learning algorithms
Xu, A. and Raginsky, M. (2017) · 2017
Cited alongside, same era.
Generalization error bounds using wasserstein distances
Lopez, A. T. and Jog, V. (2018) · 2018
Cited alongside, same era.
Tighter expected generalization error bounds via wasserstein distance
Rodríguez Gálvez, B., Bassi, G., Thobaben, R., and Skoglund, M. (2021) · 2021
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Maximum likelihood training of score-based diffusion models
Song, Y., Durkan, C., Murray, I., and Ermon, S. (2021) · 2021
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Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (2021) · 2021
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A survey on generative diffusion model
Cao, H., Tan, C., Gao, Z., Chen, G., Heng, P.-A., and Li, S. Z. (2022) · 2022
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Vector quantized diffusion model for text-to-image synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., and Guo, B. (2022) · 2022
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A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y., and Song, Z. (2019) · 2019
Cited alongside, same era.
Gradient descent finds global minima of deep neural networks
Du, S., Lee, J. D., Li, H., Wang, L., and Zhai, X. (2019) · 2019
Cited alongside, same era.
Tightening mutual information-based bounds on generalization error
Bu, Y., Zou, S., and Veeravalli, V. V. (2020) · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2020) · 2020
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P. (2021) · 2021
Cited alongside, same era.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, J. (2022) · 2022
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Diffusion art or digital forgery? investigating data replication in diffusion models
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., and Goldstein, T. (2022) · 2022
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2022) · 2022
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Characterization of excess risk for locally strongly convex population risk
Yi, M., Wang, R., and Ma, Z.-M. (2022) · 2022
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E. (2023) · 2023
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Breaking correlation shift via conditional invariant regularizer
Yi, M., Wang, R., Sun, J., Li, Z., and Ma, Z.-M. (2023) · 2023
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