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

arXiv:2503.18462v2 Announce Type: replace-cross 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 effectively captures the interplay among fidelity, diversity, and novelty in generated samples remains a persistent challenge. A recently proposed approach, the Feature Likelihood Divergence (FLD), offers a theoretically grounded and practical tool in this regard but f

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