LEARNING METHOD AND LEARNING DEVICE FOR GENERATION OF VIRTUAL FEATURE MAPS WHOSE CHARACTERISTICS ARE SAME AS OR SIMILAR TO THOSE OF REAL FEATURE MAPS BY USING GAN CAPABLE OF BEING APPLIED TO DOMAIN ADAPTATION TO BE USED IN VIRTUAL DRIVING ENVIRONMENTS

摘要

The present invention uses a Generative Adversarial Network (GAN) including a Generating Network and a Discriminating Network, which can be applied to domain adaptation used in a virtual driving environment, and a real image (Real Image) relates to a learning method for deriving a virtual feature map from a virtual image having the same or similar characteristics as a real feature map derived from (a) causing, by the learning apparatus, a generating network to apply a convolution operation to an input image to generate an output feature map having the same or similar characteristics to an actual feature map; And (b) causing the first loss unit (Loss Unit) to generate a loss with reference to an evaluation score generated by the delimiting network, corresponding to the output feature map; It is characterized in that, through the method used for the runtime input conversion, the difference between virtual and reality and the cost of annotation can be reduced.