[Submitted on 24 Mar 2025 (v1), last revised 19 Oct 2026 (this version, v2)]
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 faces notable computational limitations. In this paper, we propose PALATE, a novel framework designed to enhance the evaluation of DGMs by addressing the efficiency constraints of FLD. Our approach is based on a peculiar application of the law of total expectation to random variables representing accessible data. When integrated with the MMD baseline metric and the recently introduced DINOv3 feature extractor, PALATE provides a comprehensive evaluation framework that matches or exceeds state-of-the-art methods while offering superior computational efficiency and scalability to large-scale datasets. Through a series of experiments, we demonstrate that the PALATE enhancement constitutes a practical and computationally efficient framework for holistic DGM assessment, particularly in detecting sample memorization and evaluating generalization capabilities. The source code is provided as supplementary material.
Submission history
From: Tadeusz Dziarmaga [view email]
[v1] Mon, 24 Mar 2025 09:06:45 UTC (638 KB)
[v2] Mon, 19 Oct 2026 23:41:32 UTC (5,998 KB)
