Closed-Form Factorization of Latent Semantics in GANs

Understand

A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images.

• In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear classifiers in the latent space.
• However, they require a clear definition of the target attribute as well as the corresponding manual annotations, limiting their applications in practice.
• In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner.