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A flow-based generative model is a statistical model that explicitly models a probability distribution by leveraging the change-of-variables law of probabilities to transform a simple distribution into a complex one. It is usually used as a generative model to learn the distribution (i.e. likelihood function) of observed data, .
Implementations
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- RealNVP
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Applications
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- Point-cloud modeling
- Music generation
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References
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Category:Machine learning Category:Statistical models Category:Probabilistic models