GAN units align with object masks and causally control objects

Bau et al. identify interpretable units in a real trained Progressive GAN by scoring each channel's spatial activation against object-segmentation masks [bau-etal-2019-gan-dissection] Causally ablating or forcibly activating identified units reliably removes or adds the corresponding object (e.g. a tree, a door) in generated images [bau-etal-2019-gan-dissection] The method also locates and removes artifact-causing units and enables interactive scene editing [bau-etal-2019-gan-dissection] Identification is a quantified geometric measurement and the ablation/insertion is a direct causal intervention with measured image effects [bau-etal-2019-gan-dissection]