论文标题
产生未观察到的替代方案
Generating Unobserved Alternatives
论文作者
论文摘要
我们考虑可以将多个预测视为正确的问题，但其中只有一个被视为监督。此设置不同于回归和阶级条件生成建模设置：在前者中，每个输入都有一个独特的观察到的输出，该输出作为监督提供；在后者中，每个输入都有许多观察到的输出，许多输出作为监督。将要么的回归方法和条件生成模型应用于本设置通常会导致一个模型，该模型只能为每个输入做一个单个预测。我们探索了具有此属性的几个问题，并开发了一种可以在相同输入给定的高质量预测中产生多种高质量预测的方法。结果，它可用于生成与观察到的输出不同的高质量输出。
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.
