  In this paper, we use a tensor model based on the Higher-Order Singular Value
Decomposition (HOSVD) to discover semantic directions in Generative Adversarial
Networks. This is achieved by first embedding a structured facial expression
database into the latent space using the e4e encoder. Specifically, we discover
directions in latent space corresponding to the six prototypical emotions:
anger, disgust, fear, happiness, sadness, and surprise, as well as a direction
for yaw rotation. These latent space directions are employed to change the
expression or yaw rotation of real face images. We compare our found directions
to similar directions found by two other methods. The results show that the
visual quality of the resultant edits are on par with State-of-the-Art. It can
also be concluded that the tensor-based model is well suited for emotion and
yaw editing, i.e., that the emotion or yaw rotation of a novel face image can
be robustly changed without a significant effect on identity or other
attributes in the images.
