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Among likelihood-based approaches for deep generative modelling, variational autoencoders (VAEs) offer scalable amortized posterior inference and fast sampling.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
Earlier work this paper cites.
Elbo surgery: yet another way to carve up the variational evidence lower bound
Hoffman, M. D. and Johnson, M. J · 2016
Earlier work this paper cites.
Improving variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Oord, A. v. d., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
Cited alongside, same era.
Variational lossy autoencoder
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, R. T., Li, X., Grosse, R., and Duvenaud, D · 2018
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Later among the works it cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Later among the works it cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Later among the works it cites.
Nvae: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
Later among the works it cites.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Cited alongside, same era.
Vae with a vampprior
Tomczak, J. and Welling, M · 2018
Cited alongside, same era.
Video compression with rate-distortion autoencoders
Habibian, A., Rozendaal, T. v., Tomczak, J. M., and Cohen, T. S · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Cited alongside, same era.
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Improved denoising diffusion probabilistic models
Nichol, A. and Dhariwal, P · 2021
Closest in time.
Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
Closest in time.