Generative adversarial networks (GANs) are one of the most popular methods for generating images today. While impressive results have been validated by visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification---GAN-train and GAN-test, which approximate the recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate a number of recent GAN approaches based on these two measures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures.
@cite_35: Generative adversarial networks (GAN) are a powerful subclass of generative models. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. We conduct a neutral, multi-faceted large-scale empirical study on state-of-the art models and evaluation measures. We find that most models can reach similar scores with enough hyperparameter optimization and random restarts. This suggests that improvements can arise from a higher computational budget and tuning more than fundamental algorithmic changes. To overcome some limitations of the current metrics, we also propose several data sets on which precision and recall can be computed. Our experimental results suggest that future GAN research should be based on more systematic and objective evaluation procedures. Finally, we did not find evidence that any of the tested algorithms consistently outperforms the non-saturating GAN introduced in goodfellow2014generative .
Precision and recall measures were introduced @cite_35 in the context of GANs, by constructing a synthetic data manifold. This makes it possible to compute the distance of an image sample (generated or real) to the manifold, by finding its distance to the closest point from the manifold. In this synthetic setup, precision is defined as the fraction of the generated samples whose distance to the manifold is below a certain threshold. Recall, on the other hand, is computed by considering a set of test samples. First, the latent representation @math of each test sample @math is estimated, through gradient descent, by inverting the generator @math . Recall is then given by the fraction of test samples whose L2-distance to @math is below the threshold. High recall is equivalent to the GAN capturing most of the manifold, and high precision implies that the generated samples are close to the manifold. Although these measures bring the flavor of techniques used widely to evaluate discriminative models to GANs, they are impractical for real images as the data manifold is unknown, and their use is limited to evaluations on synthetic data @cite_35 .