Sample generation method based on GAN and adaptive transfer learning

The method of sample generation and automatic annotation based on the generative countermeasure network (GAN) and cross-domain adaptive transfer learning was studied. In this method, the adaptive transfer learning network is used to explore the intrinsic correlation of target features in infrared and visible images based on the small number of existing visible image samples, and the adaptive transfer learning network model is constructed to generate tagged target images. The problem of small number of infrared image samples and time-consuming labeling can be solved by proposed method, which provides enough sample data for subsequent multi-band cooperative target detection and recognition. Moreover, automatic standard tests were carries out on the 1 000 pieces of actual acquired and 1 000 pieces of generated armored target images ,respectively,by using the automatic standard algorithm The experimental results show that the accuracy of the actual armored target image labeling is more than 95%, and that of the generated armored target image labeling is more than 83%. The performance of classifiers trained with the mixed dataset of real images and generate images is basically the same as when using the pure real images.