Understanding how well a deep generative model captures a distribution of
high-dimensional data remains an important open challenge. It is especially
difficult for certain model classes, such as Generative Adversarial Networks
and Diffusion Models, whose models do not admit exact likelihoods. In this
work, we demonstrate that generalized empirical likelihood (GEL) methods offer
a family of diagnostic tools that can identify many deficiencies of deep
generative models (DGMs). We show, with appropriate specification of moment
conditions, that the proposed method can identify which modes have been
dropped, the degree to which DGMs are mode imbalanced, and whether DGMs
sufficiently capture intra-class diversity. We show how to combine techniques
from Maximum Mean Discrepancy and Generalized Empirical Likelihood to create
not only distribution tests that retain per-sample interpretability, but also
metrics that include label information. We find that such tests predict the
degree of mode dropping and mode imbalance up to 60% better than metrics such
as improved precision/recall.

本文研究了深度生成模型的诊断方法，提出了基于广义经验似然的方法，可以检测模型何时丢失了关键模式、发现模型的不平衡性并评估其是否能足够捕获类内多样性。