1 research outputs found
Adversarial Pixel-Level Generation of Semantic Images
Generative Adversarial Networks (GANs) have obtained extraordinary success in
the generation of realistic images, a domain where a lower pixel-level accuracy
is acceptable. We study the problem, not yet tackled in the literature, of
generating semantic images starting from a prior distribution. Intuitively this
problem can be approached using standard methods and architectures. However, a
better-suited approach is needed to avoid generating blurry, hallucinated and
thus unusable images since tasks like semantic segmentation require pixel-level
exactness. In this work, we present a novel architecture for learning to
generate pixel-level accurate semantic images, namely Semantic Generative
Adversarial Networks (SemGANs). The experimental evaluation shows that our
architecture outperforms standard ones from both a quantitative and a
qualitative point of view in many semantic image generation tasks