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Even though Generative Adversarial Networks (GANs) have shown a remarkableability to generate high-quality images, GANs do not always guarantee thegeneration of photorealistic images. Occasionally, they generate images thathave defective or unnatural objects, which are referred to as 'artifacts'.Research to investigate why these artifacts emerge and how they can be detectedand removed has yet to be sufficiently carried out. To analyze this, we firsthypothesize that rarely activated neurons and frequently activated neurons havedifferent purposes and responsibilities for the progress of generating images.In this study, by analyzing the statistics and the roles for those neurons, weempirically show that rarely activated neurons are related to the failureresults of making diverse objects and inducing artifacts. In addition, wesuggest a correction method, called 'Sequential Ablation', to repair thedefective part of the generated images without high computational cost andmanual efforts.

Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?