We introduce a new problem, that of undefined class-label (UCL) detection. For instance, if we try to classify an image of a radio as cat vs dog, there will be no well-defined class label. In contrast, in out-of-distribution (OOD) detection, we are interested in the related but different problem of identifying regions of the input space with little training data, which might result in poor classifier performance. This difference is critical: it is quite possible for there to be a region of the input space where little training data is available but where class-labels are well-defined. Likewise, there may be regions with lots of training data, but without well-defined class-labels (though in practice this would often be the result of a bug in the labelling pipeline). We note that certain methods originally intended to detect OOD inputs might actually be detecting UCL points and develop a method for training on UCL points based on a generative model of data-curation originally used to explain the cold posterior effect in Bayesian neural networks. This approach gives superior performance to past methods originally intended for OOD detection.

翻译：我们引入了一个新问题,即未定义的分类标签(UCL)检测问题。例如,如果我们试图将电台图像归类为猫类与狗类,则没有定义明确的分类标签。相反,在分配范围外的检测(OOOD)中,我们感兴趣的是相关但不同的问题,即用很少的培训数据来识别输入空间的区域,这可能导致分类性能差。这一差异至关重要:在输入空间的某个区域,几乎没有培训数据,但班级标签有明确定义。同样,有些区域可能有许多培训数据,但没有定义明确的分类标签(尽管在实践中,这往往是标签管道中的错误的结果 ) 。 我们注意到,某些原本打算检测OOD投入的方法实际上可能是检测UCL点,并且根据最初用于解释Bayesian神经网络冷藏后部效果的数据精确模型,制定UCL点培训方法。这种方法使原先用于检测OOD的以往方法具有更高的性能。