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Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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Learning multiple layers of features from tiny images, 2009
Krizhevsky, A · 2009
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Auto-encoding variational bayes, 2013
Kingma, D. P. and Welling, M · 2013
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Adam: A method for stochastic optimization, 2014
Kingma, D. P. and Ba, J · 2014
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Deep residual learning for image recognition, 2015
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop, 2015
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2015
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Improved techniques for training gans, 2016
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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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
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Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications, 2017
Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Attention is all you need, 2017
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Pixelsnail: An improved autoregressive generative model
Chen, X., Mishra, N., Rohaninejad, M., and Abbeel, P · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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An empirical model of large-batch training, 2018
McCandlish, S., Kaplan, J., Amodei, D., and Team, O. D · 2018
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Generating high fidelity images with subscale pixel networks and multidimensional upscaling, 2018
Menick, J. and Kalchbrenner, N · 2018
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Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., Ku, A., and Tran, D · 2018
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Generating long sequences with sparse transformers, 2019
Language models are few-shot learners, 2020
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
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Very deep vaes generalize autoregressive models and can outperform them on images
Child, R · 2020
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Learning energy-based models by diffusion recovery likelihood, 2020
Gao, R., Song, Y., Poole, B., Wu, Y. N., and Kingma, D. P · 2020
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Scaling laws for autoregressive generative modeling, 2020
Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., Hallacy, C., Mann, B., Radford, A., Ramesh, A., Ryder, N., Ziegler, D. M., Schulman, J., Amodei, D., and McCandlish, S · 2020
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Denoising diffusion probabilistic models, 2020
Ho, J., Jain, A., and Abbeel, P · 2020
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Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
Cited alongside, same era.
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P · 2019
Cited alongside, same era.
Improved precision and recall metric for assessing generative models, 2019
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
Cited alongside, same era.
Classification accuracy score for conditional generative models
Ravuri, S. and Vinyals, O · 2019
Cited alongside, same era.
Generating diverse high-fidelity images with vq-vae-2, 2019
Razavi, A., van den Oord, A., and Vinyals, O · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
Generative pretraining from pixels, 2020a
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Dhariwal, P., Luan, D., and Sutskever, I
Cited in the paper.
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Adversarial score matching and improved sampling for image generation, 2020
Jolicoeur-Martineau, A., Piché-Taillefer, R., des Combes, R. T., and Mitliagkas, I · 2020
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Scaling laws for neural language models, 2020
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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Diffwave: A versatile diffusion model for audio synthesis, 2020
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
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Efficient content-based sparse attention with routing transformers, 2020
Roy, A., Saffar, M., Vaswani, A., and Grangier, D · 2020
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
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Nvae: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
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