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arXiv · 2503.18462

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

Tadeusz Dziarmaga·Marcin Kądziołka·Artur Kasymov·Marcin Mazur
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Abstract

Deep generative models (DGMs) have caused a paradigm shift in the field of machine learning, yielding noteworthy advancements in domains such as image synthesis, natural language processing, and other related areas. However, a comprehensive evaluation of these models that accounts for the trichotomy between fidelity, diversity, and novelty in generated samples remains a formidable challenge. A recently introduced solution that has emerged as a promising approach in this regard is the Feature Likelihood Divergence (FLD), a method that offers a theoretically motivated practical tool, yet also exhibits some computational challenges. In this paper, we propose PALATE, a novel enhancement to the evaluation of DGMs that addresses limitations of existing metrics. Our approach is based on a peculiar application of the law of total expectation to random variables representing accessible real data. When combined with the MMD baseline metric and DINOv2 feature extractor, PALATE offers a holistic evaluation framework that matches or surpasses state-of-the-art solutions while providing superior computational efficiency and scalability to large-scale datasets. Through a series of experiments, we demonstrate the effectiveness of the PALATE enhancement, contributing a computationally efficient, holistic evaluation approach that advances the field of DGMs assessment, especially in detecting sample memorization and evaluating generalization capabilities.

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Tadeusz Dziarmaga, Marcin Kądziołka, Artur Kasymov, Marcin Mazur. 2025-03-24. PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative Models. https://arxiv.org/abs/2503.18462

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