Detecting GAN generated errors

2 Dec 2019 Xiru Zhu Fengdi Che Tianzi Yang Tzuyang Yu David Meger Gregory Dudek

Despite an impressive performance from the latest GAN for generating hyper-realistic images, GAN discriminators have difficulty evaluating the quality of an individual generated sample. This is because the task of evaluating the quality of a generated image differs from deciding if an image is real or fake... (read more)

PDF Abstract
No code implementations yet. Submit your code now

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper