论文标题
LGSQE：轻巧生成的样品质量评估
LGSQE: Lightweight Generated Sample Quality Evaluatoin
论文作者
论文摘要
尽管在评估生成模型方面进行了多产，但对单个生成样本的质量评估的研究很少。为了解决此问题，在这项工作中提出了轻巧生成的样本质量评估（LGSQE）方法。在LGSQE的训练阶段，对二元分类器进行了对真实和合成样本的训练，其中实际和合成数据分别标记为0和1。在推理阶段，分类器将软标签（从0到1）分配给每个生成的样品。软标签的值表示质量水平；也就是说，如果其软标签更接近0，则质量会更好。LGSQE可以用作质量控制的后处理模块。此外，LGSQE可用于通过汇总样品级质量来评估生成模型的性能，例如准确性，AUC，精度和回忆。实验是在CIFAR-10和MNIST上进行的，以证明LGSQE可以保留与Frechet Inception Inteption距离（FID）预测的相同性能等级顺序（FID），但复杂性的较低。
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.
