Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
arxiv(2023)
摘要
The past few years have seen impressive progress in the development of deep
generative models capable of producing high-dimensional, complex, and
photo-realistic data. However, current methods for evaluating such models
remain incomplete: standard likelihood-based metrics do not always apply and
rarely correlate with perceptual fidelity, while sample-based metrics, such as
FID, are insensitive to overfitting, i.e., inability to generalize beyond the
training set. To address these limitations, we propose a new metric called the
Feature Likelihood Divergence (FLD), a parametric sample-based metric that uses
density estimation to provide a comprehensive trichotomic evaluation accounting
for novelty (i.e., different from the training samples), fidelity, and
diversity of generated samples. We empirically demonstrate the ability of FLD
to identify overfitting problem cases, even when previously proposed metrics
fail. We also extensively evaluate FLD on various image datasets and model
classes, demonstrating its ability to match intuitions of previous metrics like
FID while offering a more comprehensive evaluation of generative models. Code
is available at https://github.com/marcojira/fld.
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