InterFaceGAN SVM boundary normals are linear, editable, and partially entangled

Shen et al. train a linear SVM per attribute (gender, age, pose, smile, eyeglasses) in PGGAN and StyleGAN latent spaces, taking each boundary normal as that attribute's direction [shen-etal-2020-interfacegan] A proven linear relation ties latent distance-to-boundary to the generated image's semantic score, with PGGAN boundaries over 95% validation accuracy [shen-etal-2020-interfacegan] Boundary-normal cosine similarity measures entanglement: gender/age/eyeglasses are correlated while pose/smile are near-independent, and StyleGAN's W space is far more disentangled than Z [shen-etal-2020-interfacegan] Conditional manipulation, projecting out a correlated direction, removes leakage while preserving the target edit [shen-etal-2020-interfacegan] Layer-wise ablation localizes each attribute to a distinct subset of StyleGAN's style layers, and W-space edits best preserve identity [shen-etal-2020-interfacegan]