はてなブックマーク

In this work, we propose a closed-form algorithm, called SeFa, for unsupervised latent Semantics Factorization in GANs. More concretely, we investigate the very first fully-connected layer used in the GAN generator. We argue that this layer actually filters out some negligible directions in the latent space and highlights the directions that are critical for image synthesis. By finding these impor