  Generative Adversarial Networks (GANs) are now widely used for
photo-realistic image synthesis. In applications where a simulated image needs
to be translated into a realistic image (sim-to-real), GANs trained on unpaired
data from the two domains are susceptible to failure in semantic content
retention as the image is translated from one domain to the other. This failure
mode is more pronounced in cases where the real data lacks content diversity,
resulting in a content \emph{mismatch} between the two domains - a situation
often encountered in real-world deployment. In this paper, we investigate the
role of the discriminator's receptive field in GANs for unsupervised
image-to-image translation with mismatched data, and study its effect on
semantic content retention. Experiments with the discriminator architecture of
a state-of-the-art coupled Variational Auto-Encoder (VAE) - GAN model on
diverse, mismatched datasets show that the discriminator receptive field is
directly correlated with semantic content discrepancy of the generated image.
