Despite significant improvements on the image generation performance of Generative Adversarial Networks (GANs), generations with low visual fidelity still have been observed. As widely used metrics for GANs focus more on the overall performance of the model, evaluation on the quality of individual generations or detection of defective generations is challenging. While recent studies try to detect featuremap units that cause artifacts and evaluate individual samples, these approaches require additional resources such as external networks or a number of training data to approximate the real data manifold. In this work, we propose the concept of local activation, and devise a metric on the local activation to detect artifact generations without additional supervision. We empirically verify that our approach can detect and correct artifact generations from GANs with various datasets. Finally, we discuss a geometrical analysis to partially reveal the relation between the proposed concept and low visual fidelity.

本研究提出了一个新的概念local activation，通过测量local activation，可以检测出GANs生成的有缺陷的图像。经过实验证明，本方法可以应用于不同数据集的GANs中，帮助用户检测和纠正有缺陷的生成图像。同时，通过几何分析，部分揭示了本研究的概念与低视觉保真度之间的关系。

生成神经网络中生成伪影的内部单元的无监督理解方法