CycleGAN

CycleGAN is a machine learning technique that enables the transformation of images from one style to another without needing paired examples. For instance, it can convert photos of horses into zebras or daytime scenes into nighttime scenes, learning these changes from separate collections of each style. It uses two neural networks that work together: one to translate images in one direction and another to reverse the translation, ensuring quality and consistency. This cycle consistency helps the system learn realistic transformations by maintaining the core content while changing the appearance, making it a powerful tool for style transfer and image synthesis.