DCGAN latent vector arithmetic yields a smiling man from face-attribute offsets

measured in 1 paper

Radford et al. train a DCGAN on aligned faces and LSUN bedrooms and apply Mikolov-style vector-offset arithmetic directly in the generator's latent Z space [radford-etal-2016-dcgan] Averaging "smiling woman" Z vectors, subtracting averaged "neutral woman," and adding averaged "neutral man" yields a Z the generator renders as a smiling man [radford-etal-2016-dcgan] Similar arithmetic is demonstrated for other attributes (e.g. eyeglasses), a direct causal latent-space manipulation [radford-etal-2016-dcgan] Results are shown qualitatively via generated image grids, with no quantitative effect-size metric reported [radford-etal-2016-dcgan]

Structure

Linear Direction

Context

latent vector arithmetic, GAN attribute manipulation, analogy-style latent offsets

Confirmed in models

DCGAN (trained on aligned & cropped celebrity faces) · DCGAN (Deep Convolutional GAN)

Method

Vector-offset analogy test, Causal interventions (steering)

Papers

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks — Radford, Alec, Metz, Luke, Chintala, Soumith2016 · arXiv:1511.06434