  An interpretable comparison of generative models requires the identification
of sample types produced more frequently by each of the involved models. While
several quantitative scores have been proposed in the literature to rank
different generative models, such score-based evaluations do not reveal the
nuanced differences between the generative models in capturing various sample
types. In this work, we attempt to solve a differential clustering problem to
detect sample types expressed differently by two generative models. To solve
the differential clustering problem, we propose a method called Fourier-based
Identification of Novel Clusters (FINC) to identify modes produced by a
generative model with a higher frequency in comparison to a reference
distribution. FINC provides a scalable stochastic algorithm based on random
Fourier features to estimate the eigenspace of kernel covariance matrices of
two generative models and utilize the principal eigendirections to detect the
sample types present more dominantly in each model. We demonstrate the
application of the FINC method to large-scale computer vision datasets and
generative model frameworks. Our numerical results suggest the scalability of
the developed Fourier-based method in highlighting the sample types produced
with different frequencies by widely-used generative models. Code is available
at \url{https://github.com/buyeah1109/FINC}
