RTM uses iterative refinement of latent codes in generative models to improve both precision and recall alongside competitive FID scores on CIFAR-10, CelebA-HQ, and few-shot datasets.
Implicit Maximum Likelihood Estimation
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abstract
Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly. We develop a simple method for estimating parameters in implicit models that does not require knowledge of the form of the likelihood function or any derived quantities, but can be shown to be equivalent to maximizing likelihood under some conditions. Our result holds in the non-asymptotic parametric setting, where both the capacity of the model and the number of data examples are finite. We also demonstrate encouraging experimental results.
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2026 1verdicts
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One Pass Is Not Enough: Recursive Latent Refinement for Generative Models
RTM uses iterative refinement of latent codes in generative models to improve both precision and recall alongside competitive FID scores on CIFAR-10, CelebA-HQ, and few-shot datasets.