The invention provides an
image conversion method based on a variation automatic
encoder and the
generative adversarial network. The method is mainly characterized by comprising the variation automatic
encoder (VAE), weight sharing, generating the
generative adversarial network (GAN) and learning, in the process, a non-monitored image is utilized to learn a bidirectional
conversion function between two image domains in an
image conversion network framework (UNIT), VAE and VAE are comprised, modeling for each
image domain is carried out through utilizing the VAE and the VAE, mutual action of an adversarial training target and a weight sharing constraint is carried out, corresponding images are generated in the two image domains, the conversion image is associated with an input image of each domain, and image reconstruction flow and
image conversion flow problems can be solved through training network combination. The method is advantaged in that the non-monitoring image is utilized to the image conversion framework, images in the two domains having not any relations are made to accomplish conversion, a corresponding training
data set formed by the images is not needed, efficiency and practicality are improved, and the method can be developed to non-monitoring language conversion.