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Traditional wisdom in generative modeling literature is that spurious samplesthat a model can generate are errors and they should be avoided. Recentresearch, however, has shown interest in studying or even exploiting suchsamples instead of eliminating them. In this paper, we ask the question whethersuch samples can be eliminated all together without sacrificing coverage of thegenerating distribution. For the class of models we consider, we experimentallydemonstrate that this is not possible without losing the ability to model someof the test samples. While our results need to be confirmed on a broader set ofmodel families, these initial findings provide partial evidence that spurioussamples share structural properties with the learned dataset, which, in turn,suggests they are not simply errors but a feature of deep generative nets.

Spurious samples in deep generative models: bug or feature?