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SeFa — Finding Semantic Vectors in Latent Space for GANs
Paper Explained: SeFa — Closed-Form Factorization of Latent Semantics in GANs
Motivation
The generator in GANs usually takes a randomly sampled latent vector z as the input and generates a high-fidelity image. By changing the latent vector z, we can change the output image.
However, in order to change a specific attribute in the output image (e.g. hair color, facial expression, pose, gender, etc.), we need to know the specific direction in which to move our latent vector z.
Some previous works have tried to interpret the latent semantics in a supervised fashion. They usually label the dataset and train an attribute classifier to predict the labels of the images, and then calculate the direction vectors of the latent code z for each label. Even though there were some unsupervised methods for this task, most of them require model training and data sampling.
Nonetheless, this paper proposed a closed-form and unsupervised method, named SeFa, to let us find out these direction vectors for altering different attributes in the…
