In the limitations section, the paper applies noise injection to the released CycleGAN for the horse-to-zebra translation task. The baseline CycleGAN's FID rises from 76.92 (clean) to 180.82 (Gaussian noise at sigma_e^2=0.16). The authors note that the noise-injected CycleGAN struggles with clean inputs (FID 283.97) and produces visible distortions around the zebra's head and legs despite better FID scores on noisy inputs, concluding that GNI is not a straightforward plug-and-play solution for bidirectional i2i models.
Evidence
correlational
Key metric
FID: 76.92 (clean) to 180.82 (sigma_e^2=0.16); noise-injected FID on clean: 283.97
Caveat
The authors explicitly state that for bidirectional i2i translation models, GNI 'cannot be considered a straightforward plug-and-play solution' and that 'adaptations in network architectures and/or loss functions might be requisite to achieve desired results.'