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。

基于无监督评估的GAN样本质量：引入TTJac评分