The article introduces a new method called statistical distance latent regulation loss to improve the recovery of latent codes in generative models. By considering the distribution of all elements simultaneously, this method increases the likelihood that the recovered latent code was originally sampled from a specific distribution. The researchers also propose a test to verify if the recovered latent codes follow the expected distribution. Compared to other methods, using the statistical distance latent regulation loss leads to more accurate recovery of latent codes in gradient descent-based processes.