Many real-world vision problems suffer from inherent ambiguities. In clinical
applications for example, it might not be clear from a CT scan alone which
particular region is cancer tissue. Therefore a group of graders typically
produces a set of diverse but plausible segmentations. We consider the task of
learning a distribution over segmentations given an input. To this end we
propose a generative segmentation model based on a combination of a U-Net with
a conditional variational autoencoder that is capable of efficiently producing
an unlimited number of plausible hypotheses. We show on a lung abnormalities
segmentation task and on a Cityscapes segmentation task that our model
reproduces the possible segmentation variants as well as the frequencies with
which they occur, doing so significantly better than published approaches.
These models could have a high impact in real-world applications, such as being
used as clinical decision-making algorithms accounting for multiple plausible
semantic segmentation hypotheses to provide possible diagnoses and recommend
further actions to resolve the present ambiguities.

通过使用结合了 U-Net 和条件变分自编码器的生成式分割模型，我们可以有效地产生无限数量的合理假设，以学习分割的分布，该模型可在肺部和城市街景分割任务中显着优于先前的方法，对于临床决策过程中存在的歧义问题，该模型能提供多种可能性的语义分割假设进行诊断并建议需要进一步的行动。