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arXiv AI By Tadeusz Dziarmaga, Marcin K\k{a}dzio{\l}ka, Laura Musio{\l}, Patryk Marsza{\l}ek, Artur Kasymov, Piotr Ko\'scielniak, Marcin Mazur

PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models

Read the original on arXiv AI →

PALATE is a new framework that improves the evaluation of deep generative models by applying the law of total expectation to accessible data. It builds on the Feature Likelihood Divergence (FLD) method but overcomes FLD’s computational limits, integrating with the MMD baseline metric and the DINOv3 feature extractor. Experiments show PALATE matches or surpasses state‑of‑the‑art methods while being more efficient and scalable, especially for detecting memorization and assessing generalization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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