We consider problems where multiple predictions can be considered correct, but only one of them is given as supervision. This setting differs from both the regression and class-conditional generative modelling settings: in the former, there is a unique observed output for each input, which is provided as supervision; in the latter, there are many observed outputs for each input, and many are provided as supervision. Applying either regression methods and conditional generative models to the present setting often results in a model that can only make a single prediction for each input. We explore several problems that have this property and develop an approach that can generate multiple high-quality predictions given the same input. As a result, it can be used to generate high-quality outputs that are different from the observed output.
翻译:我们考虑了多种预测可以被视为正确的问题,但只有一种预测可以作为监督。这种设定与回归和等级条件的基因模型设置不同:在前者,每种输入都有独特的观察产出,作为监督提供;在后者,每种输入有许多观察产出,许多作为监督提供。在目前的设置中应用回归方法和有条件的基因模型往往导致一种只能对每项输入作出单一预测的模型。我们探讨了具有这种属性的一些问题,并制定了一种办法,根据同样的投入可以产生多重高质量的预测。因此,可以用来产生与所观察到的产出不同的高质量产出。