Attribute-Based Facial Image Manipulation on Latent Space

Using machine learning to generate images has become more mature, especially the images produced using a Generative Adversarial Network. Unfortunately, the complicated architecture of those models makes it difficult for us to ensure the output images' diversity and controllability without introducing little embarrassment in implementation. Therefore, some researchers try to edit the latent codes generated by a given learning model directly on the latent space for manipulating the output image by simply inputting the new latent codes into the original model without changing the model's structure and learned parameters. However, the methods mentioned above faced the problems that the size of latent space cannot be too large or the trouble-some of features entanglement. In this work, we propose an approach to conquer the problems mentioned above, which is to compress the original latent space to better the applicability and usability of the methods limited by the size of the latent space. Compared with the existing methods, this method can be applied to more models and still reach the target of image manipulation. © 2021 IEEE.