# Can GAN's be used to fill in gaps?

Is it possible to make a Generative Adversarial Network that, given a fragment of a valid piece of data, can identify the patterns involved and generate something close to the original piece of data? From what I understand, typical GANs' generators take in a noise vector and output the generated data, but would training a GAN generator with fragmented data as input and complete data as the label? And would there be problems with training the discriminator using the same dataset?

In a conditional GAN, the generator is given both the noise vector $$z$$ and also the conditional input $$x$$ (the fragment of valid data), and is supposed to output a plausible reconstruction of the whole data. The discriminator GAN can also be conditioned on the available data $$x$$, and tries to judge if the reconstruction is real or fake.