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We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio.
Signal estimation from modified short-time fourier transform
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TIMIT Acoustic-Phonetic Continuous Speech Corpus LDC93S1
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Generative adversarial nets
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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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A recurrent latent variable model for sequential data
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., and Bengio, Y · 2015
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Krishnan, R. G., Shalit, U., and Sontag, D · 2015
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Reed, S. E., Zhang, Y., Zhang, Y., and Lee, H · 2015
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Disentangling factors of variation in deep representation using adversarial training
Mathieu, M. F., Zhao, J. J., Zhao, J., Ramesh, A., Sprechmann, P., and LeCun, Y · 2016
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Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., and Torralba, A · 2016
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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Bouchacourt, D., Tomioka, R., and Nowozin, S · 2017
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Factorized variational autoencoders for modeling audience reactions to movies
Deng, Z., Navarathna, R., Carr, P., Mandt, S., Yue, Y., Matthews, I., and Mori, G · 2017
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Unsupervised learning of disentangled representations from video
Denton, E. and Birodkar, V · 2017
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Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Sequential neural models with stochastic layers
Fraccaro, M., Sønderby, S. K., Paquet, U., and Winther, O · 2016
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