This project implements a transparent latent-space GAN (TL-GAN) approach that enables controlled image synthesis and editing by learning interpretable directions in a pre-trained GAN’s latent space. It uses a coupled CNN feature extractor network to discover feature axes, allowing users to morph image attributes like age, gender, or facial features by moving along specific latent directions. The method bridges unsupervised GAN training with supervised feature discovery.