GAN Latent Space Arithmetic

Module: Generative AI | Difficulty: Advanced

Linear Direction Finding

InterfaceGAN

Find hyperplane separating attributes.

StyleGAN Editing

where controls edit strength.

Latent Space Properties

| Space | Linearity | Editability | Reconstruction | |-------|-----------|-------------|----------------| | | Low | Low | Medium | | | Medium | High | Medium | | | High | High | High |

def find_edit_direction(G, E, images, attr_labels, layer=-1):
    codes = E(images)
    pos_codes = codes[attr_labels == 1]
    neg_codes = codes[attr_labels == 0]
    direction = pos_codes[:, layer].mean(0) - neg_codes[:, layer].mean(0)
    return direction / direction.norm()

Research Insight: The disentanglement of space is an emergent property of StyleGAN's architecture, not an explicit training objective. This suggests that architectural inductive biases are more effective than explicit disentanglement losses.