Generative Adversarial Networks for Image Synthesis

Generative Adversarial Networks (GANs) have made significant strides in the synthesis of images, particularly in sketch-to-image conversion when we talk about Conditional GANs (cGANs) and CycleGANs. This study combines Conditional GANs (cGANs) and CycleGANs to improve sketch-to-real picture synthesis on the edge2shoes dataset. CycleGANs allow transformations using unpaired data, providing more flexibility, while cGANs use paired data for accurate input-output mappings. This work investigates a novel hybrid method to enhance quality, adaptability, and generalization in sketch-to-image tasks by combining the best features of both methodologies. The findings offer insights for future developments in visual domain adaptation and image translation.