Research On Latent Space Semantic Representation Algorithm Based On Generative Adversarial Network

The study of latent space semantic representation algorithms based on Generative Adversarial Network(GAN)is one of the current research topics in implicit image representation,and it has a wide range of applications in digital content generation.To address the problem that the accuracy of the inverse mapping of images in latent space is insufficient,which makes the reconstructed image and the input image have obvious feature differences,we achieve the accurate inverse mapping of real images in latent space and the semantic editing based on latent space by optimizing the encoder structure of latent codes,the embedding optimization of generators,and the selection of loss functions.In particular,the following research work has been carried out and completed.(1)To address the problem of inaccurate semantic representation after inverse mapping of real images to the latent space of StyleGAN,a style-based encoder network is proposed to map real images to the extended latent space W+ of StyleGAN to achieve accurate inverse mapping and semantic representation of real images.The encoder network uses U-Net as a framework to construct the w+ latent vectors using the generated feature vectors of three scale sizes,jointly trained with pixel-by-pixel loss,perceptual loss and personal identity loss.Experimental results show that the algorithm is able to achieve an accurate inverse mapping of the image to the W+latent space by directly combining the w+ latent vectors through a style-based encoder network,and supports semantic editing by changing the latent code.(2)To further improve the accuracy of real image inverse mapping in spatial semantic representation,a two-stage inverse mapping method based on latent spatial embedding expansion is proposed: the first stage an in-domain guided encoder is obtained by supervised training,and pixel loss and perceptual loss is introduced.The pixel loss is used to align the encoded latent codes in the pixel domain,and the perceptual loss is used to guide the encoded latent codes in the semantic domain after the inverse mapping.The second stage the encoder output is used as the initial latent code to fine-tune the latent code generated by the encoder,which extends the latent space of StyleGAN to achieve a highly accurate inverse mapping of the real image and improve the quality of the reconstructed image after performing semantic editing.(3)The design of an engineering application demonstration of semantic manipulation of real images was carried out,and a demonstration tool based on the PyQt5 framework for semantic manipulation assistance was developed.The tool is cross-platform and supports Windows,Linux or macOS operating systems.It has two main functions: firstly it can complete and present the reconstruction result in real time after the inverse mapping of the input real image;secondly it performs semantic manipulation of the reconstructed image in real time.In addition,the tool opens up the interface to add custom algorithms for subsequent algorithm extensions and upgrades.