Explanations forthisphenomenon arecontroversial: Whilemostworksattribute the artifacts to the generator, other works point to the discriminator. We take a sober look at those explanations and provide insights on what makes proposed measures against high-frequency artifacts effective.

Withthetwo-branch architecture, itnaturally incorporates pre-trained moving statistics in BN layers during fine-tuning, exploiting more priorknowledge ofpre-trained networks.

Assuming that small spatial structures are more-or-less consistent across these measurements, these methods project to a local low-rank approximation of the data [37,31].

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On that basis, we hypothesize that these two problems stem from the conflict between the KL regularization inELBo andthefunction definition oftheprior distribution. Assuch, wepropose a novel regularization to substitute the KL regularization in ELBo for VAEs, which isbased on the density gapbetween the aggregated posterior distribution and the prior distribution.