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Standard formulations of GANs, where a continuous function deforms aconnected latent space, have been shown to be misspecified when fittingdifferent classes of images. In particular, the generator will necessarilysample some low-quality images in between the classes. Rather than modifyingthe architecture, a line of works aims at improving the sampling quality frompre-trained generators at the expense of increased computational cost. Buildingon this, we introduce an additional network to predict latent importanceweights and two associated sampling methods to avoid the poorest samples. Thisidea has several advantages: 1) it provides a way to inject disconnectednessinto any GAN architecture, 2) since the rejection happens in the latent space,it avoids going through both the generator and the discriminator, savingcomputation time, 3) this importance weights formulation provides a principledway to reduce the Wasserstein's distance to the target distribution. Wedemonstrate the effectiveness of our method on several datasets, both syntheticand high-dimensional.

Latent reweighting, an almost free improvement for GANs