FreezeG applies transfer learning by freezing the early layers of a pre-trained generator to reuse high-level features for image-to-image translation. The method projects input images into the learned latent space then propagates them through the generator to produce target images. It demonstrates style manipulation and domain adaptation on datasets like AFHQ, FFHQ, and various face-to-art translation tasks.