1. TL-GAN matches feature axes in the latent space to generate images without fine-tuning the neural network. 2. It discovers correlations between the latent vector Z and image labels by applying multivariate linear regression and normalizing the coefficients. 3. The vectors are then adjusted to be orthogonal, allowing different properties to be matched while labeling unlabeled data to add descriptions.