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by tonic_section
1932 days ago
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You mixed up implicit and explicit models. For anyone interested in the difference - implicit models such as GANs don't allow you to evaluate the probability density over datapoints - you can only sample from some surrogate model of the distribution learned by minimizing some 'distance' between the surrogate and the true empirical distribution. 'Explicit' models (I think this term is nonstandard) parameterize the density directly and modify the parameters via maximum likelihood. This allows one in theory to both directly evaluate the density and sample from the learned distribution. VAEs (only give a lower bound on the density), autoregressive models, and normalizing flows all fall under this category. Note that while it is theoretically possible for 'explicit' models to go in both directions (sample and evaluate), one direction may be much more efficient than the other for certain models. e.g. for autoregressive models you can read the first two pages of [1] for a good explanation of why. [1]: https://arxiv.org/abs/2102.11495 |
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