Flow-based generative model

A flow-based generative model is a type of machine learning model that generates data by learning invertible mappings between complex data distributions and simple latent variables. These models enable exact likelihood computation and efficient sampling, distinguishing them from other generative approaches.

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Variational diffusion model

A variational diffusion model is a type of generative model that combines principles from variational inference and diffusion processes to generate data through a controlled stochastic process. It is used primarily in machine learning to model complex data distributions by gradually transforming noise into structured data.

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VQ-VAE

VQ-VAE (Vector Quantized Variational Autoencoder) is a generative model architecture that combines discrete latent representations with variational autoencoders, enabling efficient learning and synthesis of complex data like images and audio.

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