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GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

ICLR 2019 · Image generation › GANs · approach: GAN latent optimization and evaluation · 179 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Ablate 20 manually identified artifact-causing units (ablation)Progressive GAN · LSUN outdoor church imagesFID: 43.16 -> 27.14Helped
Ablate 20 randomly chosen units (ablation)Progressive GAN · LSUN outdoor church imagesFID: 43.16 -> 43.17No clear effect
Ablate 20 manually identified artifact-causing units (ablation)Progressive GAN · LSUN outdoor church imagesHuman preference score: 72.4% vs 49.9%Helped
Ablate the 20 highest-FID units (ablation)Progressive GAN · LSUN outdoor church imagesFID: 43.16 -> 27.6Helped
Ablate the union of manually chosen and highest-FID artifact units (30 total) (ablation)Progressive GAN · LSUN outdoor church imagesFID: 43.16 -> 26.1Helped
Add minibatch standard-deviation statistics to the discriminatorProgressive GAN · LSUN scene datasetsNumber of object, part, and material types matched by units: more than 40%Helped
Add pixelwise normalizationProgressive GAN with minibatch standard-deviation statistics · LSUN scene datasetsNumber of units matching semantic classes: 19%Helped
Ablate 20 causal units for people, curtains, or windows (ablation)Conference room Progressive GAN · Conference room scenesObject pixelsHelped
Ablate 20 causal units for tables or chairs (ablation)Conference room Progressive GAN · Conference room scenesObject removalMixed
Ablate 20 window-causal units (ablation)Progressive GANs trained on different scene categories · Bedroom scenesWindow removalHurt
Insert 20 causal door units at a featuremap locationChurch-scene Progressive GAN · Church scenesDoor insertionMixed

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