Despite prolific work on evaluating generative models, little research has been done on the quality evaluation of an individual generated sample. To address this problem, a lightweight generated sample quality evaluation (LGSQE) method is proposed in this work. In the training stage of LGSQE, a binary classifier is trained on real and synthetic samples, where real and synthetic data are labeled by 0 and 1, respectively. In the inference stage, the classifier assigns soft labels (ranging from 0 to 1) to each generated sample. The value of soft label indicates the quality level; namely, the quality is better if its soft label is closer to 0. LGSQE can serve as a post-processing module for quality control. Furthermore, LGSQE can be used to evaluate the performance of generative models, such as accuracy, AUC, precision and recall, by aggregating sample-level quality. Experiments are conducted on CIFAR-10 and MNIST to demonstrate that LGSQE can preserve the same performance rank order as that predicted by the Frechet Inception Distance (FID) but with significantly lower complexity.
Cite
@article{arxiv.2211.04590,
title = {LGSQE: Lightweight Generated Sample Quality Evaluatoin},
author = {Ganning Zhao and Vasileios Magoulianitis and Suya You and C. -C. Jay Kuo},
journal= {arXiv preprint arXiv:2211.04590},
year = {2022}
}