On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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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.
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