CycleGAN is a type of generative adversarial network (GAN) designed for image-to-image translation without paired examples. It learns to convert images from one domain to another, such as transforming photos of horses into zebras and vice versa, by using two sets of images from different domains. The key innovation of CycleGAN is the cycle consistency loss, which ensures that an image translated to the other domain can be converted back to its original form. This approach allows for effective learning even when only unpaired datasets are available, making it useful in various applications like style transfer and domain adaptation.