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Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio.
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J. Engel, M. Hoffman, and A. Roberts, “Latent constraints: Learning to generate conditionally from unconditional generative models,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=Sy8XvGb0-
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A. Roberts, J. Engel, C. Raffel, C. Hawthorne, and D. Eck, “A hierarchical latent vector model for learning long-term structure in music,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, 10–15 Jul 2018, pp. 4364–4373. [Online]. Available: http://proceedings.mlr.press/v80/roberts18a.html
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M. Dinculescu, J. Engel, and A. Roberts, Eds., MidiMe: Personalizing a MusicVAE model with user data , 2019
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2021
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Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, “Diffwave: A versatile diffusion model for audio synthesis,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=a-xFK8Ymz5J
2021
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A. Ramesh, M. Pavlov, G. Goh, S. Gray, M. Chen, R. Child, V. Misra, P. Mishkin, G. Krueger, S. Agarwal, and I. Sutskever, “Dall·e: Creating images from text,” OpenAI blog , 2021. [Online]. Available: https://openai.com/blog/dall-e
2021
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