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Normalizing flows are a powerful class of generative models demonstrating strong performance in several speech and vision problems.
Probability Density Distillation with Generative Adversarial Networks for High-Quality Parallel Waveform Generation
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Nice: Non-linear independent components estimation
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Nips 2016 tutorial: Generative adversarial networks
Goodfellow, I · 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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Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., Courville, A., and Bengio, Y · 2016
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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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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P · 2016
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Deep model compression: Distilling knowledge from noisy teachers
Sau, B. and Balasubramanian, V · 2016
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Neural autoregressive distribution estimation
Uria, B., Côté, M.-A., Gregor, K., Murray, I., and Larochelle, H · 2016
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van den Oord, A., Kalchbrenner, N., Espeholt, L., kavukcuoglu, k., Vinyals, O., and Graves, A · 2016
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Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E. and Timofte, R · 2017
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Deep voice: Real-time neural text-to-speech
Arık, S. Ö., Chrzanowski, M., Coates, A., Diamos, G., Gibiansky, A., Kang, Y., Li, X., Miller, J., Ng, A., Raiman, J., Sengupta, S., and Shoeybi, M · 2017
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Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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The lj speech dataset
Ito, K. and Johnson, L · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W · 2017
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Enhanced deep residual networks for single image super-resolution
Lim, B., Son, S., Kim, H., Nah, S., and Lee, K. M · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
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Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G · 2019
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Scalable reversible generative models with free-form continuous dynamics
Grathwohl, W., Chen, R. T. Q., Bettencourt, J., and Duvenaud, D · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P · 2019
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Invertible convolutional flow
Karami, M., Schuurmans, D., Sohl-Dickstein, J., Dinh, L., and Duckworth, D · 2019
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FloWaveNet : A generative flow for raw audio
Kim, S., Lee, S.-G., Song, J., Kim, J., and Yoon, S · 2019
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Melgan: Generative adversarial networks for conditional waveform synthesis
Kumar, K., Kumar, R., de Boissiere, T., Gestin, L., Teoh, W. Z., Sotelo, J., de Brébisson, A., Bengio, Y., and Courville, A. C · 2019
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Huang, C.-W., Krueger, D., Lacoste, A., and Courville, A · 2018
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Kalchbrenner, N., Elsen, E., Simonyan, K., Noury, S., Casagrande, N., Lockhart, E., Stimberg, F., van den Oord, A., Dieleman, S., and Kavukcuoglu, K · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Ping, W., Peng, K., Gibiansky, A., Arik, S. O., Kannan, A., Narang, S., Raiman, J., and Miller, J · 2018
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Waveglow: A flow-based generative network for speech synthesis, 2018
Prenger, R., Valle, R., and Catanzaro, B · 2018
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Mode seeking generative adversarial networks for diverse image synthesis
Mao, Q., Lee, H.-Y., Tseng, H.-Y., Ma, S., and Yang, M.-H · 2019
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Ping, W., Peng, K., and Chen, J · 2019
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Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., and Liu, T.-Y · 2019
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Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Serrà, J., Pascual, S., and Segura Perales, C · 2019
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Yang, G., Huang, X., Hao, Z., Liu, M.-Y., Belongie, S., and Hariharan, B · 2019
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Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis
Kong, J., Kim, J., and Bae, J · 2020
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Nanoflow: Scalable normalizing flows with sublinear parameter complexity, 2020
Lee, S., Kim, S., and Yoon, S · 2020
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Ntire 2020 challenge on real-world image super-resolution: Methods and results
Lugmayr, A., Danelljan, M., and Timofte, R · 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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Fastspeech 2: Fast and high-quality end-to-end text to speech, 2020
Ren, Y., Hu, C., Tan, X., Qin, T., Zhao, S., Zhao, Z., and Liu, T.-Y · 2020
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Yamamoto, R., Song, E., and Kim, J.-M · 2020
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Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2021
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2021
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