Learning method and learning device for runtime input transformation of real image on real world into virtual image on virtual world, to be used for object detection on real images, by using cycle GAN capable of being applied to domain adaptation

摘要

A method for learning a runtime input transformation of real images into virtual images by using a cycle GAN capable of being applied to domain adaptation is provided. The method can be also performed in virtual driving environments. The method includes steps of: (a) (i) instructing first transformer to transform a first image to second image, (ii-1) instructing first discriminator to generate a 1_1-st result, and (ii-2) instructing second transformer to transform the second image to third image, whose characteristics are same as or similar to those of the real images; (b) (i) instructing the second transformer to transform a fourth image to fifth image, (ii-1) instructing second discriminator to generate a 2_1-st result, and (ii-2) instructing the first transformer to transform the fifth image to sixth image; (c) calculating losses. By the method, a gap between virtuality and reality can be reduced, and annotation costs can be reduced.