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Method using a discriminator to force the latent distribution to match a target distribution, avoiding the posterior collapse problem.

Probabilistic approach assuming that observed data are generated from unobserved latent variables following a specific distribution.

Parametric distribution learned by the encoder that approximates the true posterior of latent variables conditioned on input data.

Conditional distribution modeled by the decoder that generates output data from sampled latent variables.

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