SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial
  Network for an end-to-end image translation
Iman Abbasnejad Fabio Zambetta Flora Salim Timothy Wiley Jeffrey Chan Russell Gallagher Ehsan Abbasnejad
SCONE-GAN presents an end-to-end image translation, which is shown to be
effective for learning to generate realistic and diverse scenery images. Most
current image-to-image translation approaches are devised as two mappings: a
translation from the source to target domain and another to represent its
inverse. While successful in many applications, these approaches may suffer
from generating trivial solutions with limited diversity. That is because these
methods learn more frequent associations rather than the scene structures. To
mitigate the problem, we propose SCONE-GAN that utilises graph convolutional
networks to learn the objects dependencies, maintain the image structure and
preserve its semantics while transferring images into the target domain. For
more realistic and diverse image generation we introduce style reference image.
We enforce the model to maximize the mutual information between the style image
and output. The proposed method explicitly maximizes the mutual information
between the related patches, thus encouraging the generator to produce more
diverse images. We validate the proposed algorithm for image-to-image
translation and stylizing outdoor images. Both qualitative and quantitative
results demonstrate the effectiveness of our approach on four dataset.