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.

提出了一种新的评估指标 TTJac Score，用于以一种无需数据的方式评估合成图像的逼真度，通过建立理论方法和引入特征提取器和张量列车进行离散函数逼近的方法有效评估生成样本的质量，并展示了其在 StyleGAN 2 和 StyleGAN 2 ADA 模型上的应用实验结果，可用于改善逼真度和可变性之间的权衡。