Unsupervised evaluation of GAN sample quality: Introducing the TTJac
  Score
Egor Sevriugov Ivan Oseledets
Evaluation metrics are essential for assessing the performance of generative
models in image synthesis. However, existing metrics often involve high memory
and time consumption as they compute the distance between generated samples and
real data points. In our study, the new evaluation metric called the "TTJac
score" is proposed to measure the fidelity of individual synthesized images in
a data-free manner. The study first establishes a theoretical approach to
directly evaluate the generated sample density. Then, a method incorporating
feature extractors and discrete function approximation through tensor train is
introduced to effectively assess the quality of generated samples. Furthermore,
the study demonstrates that this new metric can be used to improve the
fidelity-variability trade-off when applying the truncation trick. The
experimental results of applying the proposed metric to StyleGAN 2 and StyleGAN
2 ADA models on FFHQ, AFHQ-Wild, LSUN-Cars, and LSUN-Horse datasets are
presented. The code used in this research will be made publicly available
online for the research community to access and utilize.