To turn photos into paintings or paintings, into photos, normal GANs require a pair of images. A CycleGAN is a type of GAN network that can translate an image from one domain X, to another domain Y, without the need for paired images. A CycleGAN tries to learn a generator network, which, in turn, learns two mappings. Instead of training a single generator network used in most of the GANs, CycleGANs train two generator and two discriminator networks.
There are two generator networks in a CycleGAN, which are as follows:
- Generator A: Learns a mapping
, where X is the source domain and Y is the target domain. It takes an image from the source domain A, and converts it into an image that is similar to an image from the target domain B. Basically, the aim of the network is to learn a mapping so that G(X) is similar to Y. - Generator B: Learns a mapping
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