  The massive developments of generative model frameworks require principled
methods for the evaluation of a model's novelty compared to a reference
dataset. While the literature has extensively studied the evaluation of the
quality, diversity, and generalizability of generative models, the assessment
of a model's novelty compared to a reference model has not been adequately
explored in the machine learning community. In this work, we focus on the
novelty assessment for multi-modal distributions and attempt to address the
following differential clustering task: Given samples of a generative model
$P_\mathcal{G}$ and a reference model $P_\mathrm{ref}$, how can we discover the
sample types expressed by $P_\mathcal{G}$ more frequently than in
$P_\mathrm{ref}$? We introduce a spectral approach to the differential
clustering task and propose the Kernel-based Entropic Novelty (KEN) score to
quantify the mode-based novelty of $P_\mathcal{G}$ with respect to
$P_\mathrm{ref}$. We analyze the KEN score for mixture distributions with
well-separable components and develop a kernel-based method to compute the KEN
score from empirical data. We support the KEN framework by presenting numerical
results on synthetic and real image datasets, indicating the framework's
effectiveness in detecting novel modes and comparing generative models. The
paper's code is available at: www.github.com/buyeah1109/KEN
