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Variational autoencoders (VAEs) are one of the powerful likelihood-based generative models with applications in many domains.
Cognitive science , 1985
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Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
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A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., and Teh, Y.-W · 2006
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Classification using discriminative restricted boltzmann machines
Larochelle, H. and Bengio, Y · 2008
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Learning multiple layers of features from tiny images, 2009
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Deep boltzmann machines
Salakhutdinov, R. and Hinton, G · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Monte Carlo theory, methods and examples
Owen, A. B · 2013
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Generative Adversarial Networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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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., Mohamed, S., Rezende, D. J., 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2015
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Towards conceptual compression
Gregor, K., Besse, F., Rezende, D. J., Danihelka, I., and Wierstra, D · 2016
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PixelVAE: A latent variable model for natural images
Gulrajani, I., Kumar, K., Ahmed, F., Taiga, A. A., Visin, F., Vazquez, D., and Courville, A · 2016
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Hoffman, M. D. and Johnson, M. J · 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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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
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Discrete variational autoencoders
Rolfe, J. T · 2016
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Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
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Pixel recurrent neural networks
Van Den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
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Variational lossy autoencoder
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2017
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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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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks
Mescheder, L., Nowozin, S., and Geiger, A · 2017
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Searching for activation functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
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Energy-inspired models: Learning with sampler-induced distributions
Lawson, J., Tucker, G., Dai, B., and Ranganath, R · 2019
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Coco-gan: generation by parts via conditional coordinating
Lin, C. H., Chang, C.-C., Chen, Y.-S., Juan, D.-C., Wei, W., and Chen, H.-T · 2019
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BIVA: A very deep hierarchy of latent variables for generative modeling
Maaløe, L., Fraccaro, M., Liévin, V., and Winther, O · 2019
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Autoregressive energy machines
Nash, C. and Durkan, C · 2019
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Quality aware generative adversarial networks
Parimala, K. and Channappayya, S · 2019
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Generating diverse high-fidelity images with vq-vae-2
Razavi, A., van den Oord, A., and Vinyals, O · 2019
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Neural discrete representation learning
Van Den Oord, A., Vinyals, O., et al · 2017
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Convolutional image captioning
Aneja, J., Deshpande, A., and Schwing, A. G · 2018
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Discriminator rejection sampling
Azadi, S., Olsson, C., Darrell, T., Goodfellow, I., and Odena, A · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Diagnosing and enhancing vae models
Dai, B. and Wipf, D · 2018
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Latent constraints: Learning to generate conditionally from unconditional generative models
Engel, J., Hoffman, M., and Roberts, A · 2018
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Som-vae: Interpretable discrete representation learning on time series
Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., and Rätsch, G · 2018
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Variational autoencoder with implicit optimal priors
Takahashi, H., Iwata, T., Yamanaka, Y., Yamada, M., and Yagi, S · 2019
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Metropolis-hastings generative adversarial networks
Turner, R., Hung, J., Frank, E., Saatchi, Y., and Yosinski, J · 2019
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On the necessity and effectiveness of learning the prior of variational auto-encoder
Xu, H., Chen, W., Lai, J., Li, Z., Zhao, Y., and Pei, D · 2019
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Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling
Che, T., Zhang, R., Sohl-Dickstein, J., Larochelle, H., Paull, L., Cao, Y., and Bengio, Y · 2020
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Jukebox: A generative model for music
Dhariwal, P., Jun, H., Payne, C., Kim, J. W., Radford, A., and Sutskever, I · 2020
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Flow contrastive estimation of energy-based models
Gao, R., Nijkamp, E., Kingma, D. P., Xu, Z., Dai, A. M., and Wu, Y. N · 2020
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From variational to deterministic autoencoders
Ghosh, P., Sajjadi, M. S. M., Vergari, A., Black, M., and Scholkopf, B · 2020
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Joint training of variational auto-encoder and latent energy-based model
Han, T., Nijkamp, E., Zhou, L., Pang, B., Zhu, S.-C., and Wu, Y. N · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Semi-supervised learning with normalizing flows
Izmailov, P., Kirichenko, P., Finzi, M., and Wilson, A. G · 2020
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Training generative adversarial networks with limited data
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T · 2020
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Learning latent space energy-based prior model
Pang, B., Han, T., Nijkamp, E., Zhu, S.-C., and Wu, Y. N · 2020
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Adversarial latent autoencoders
Pidhorskyi, S., Adjeroh, D. A., and Doretto, G · 2020
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Waveflow: A compact flow-based model for raw audio
Ping, W., Peng, K., Zhao, K., and Song, Z · 2020
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NVAE: A deep hierarchical variational autoencoder
Vahdat, A. and Kautz, J · 2020
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Undirected graphical models as approximate posteriors
Vahdat, A., Andriyash, E., and Macready, W. G · 2020
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Perceptual generative autoencoders
Zhang, Z., Zhang, R., Li, Z., Bengio, Y., and Paull, L · 2020
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Very deep {vae}s generalize autoregressive models and can outperform them on images
Child, R · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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{VAEBM}: A symbiosis between variational autoencoders and energy-based models
Xiao, Z., Kreis, K., Kautz, J., and Vahdat, A · 2021
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