  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. In particular, we take a closer look into the generation
mechanism of GANs and further propose a closed-form factorization algorithm for
latent semantic discovery by directly decomposing the pre-trained weights. With
a lightning-fast implementation, our approach is capable of not only finding
semantically meaningful dimensions comparably to the state-of-the-art
supervised methods, but also resulting in far more versatile concepts across
multiple GAN models trained on a wide range of datasets.
