The performance of modern deep learning-based systems dramatically depends on the quality of input objects. For example, face recognition quality would be lower for blurry or corrupted inputs. However, it is hard to predict the influence of input quality on the resulting accuracy in more complex scenarios. We propose an approach for deep metric learning that allows direct estimation of the uncertainty with almost no additional computational cost. The developed \textit{ScaleFace} algorithm uses trainable scale values that modify similarities in the space of embeddings. These input-dependent scale values represent a measure of confidence in the recognition result, thus allowing uncertainty estimation. We provide comprehensive experiments on face recognition tasks that show the superior performance of ScaleFace compared to other uncertainty-aware face recognition approaches. We also extend the results to the task of text-to-image retrieval showing that the proposed approach beats the competitors with significant margin.

翻译：现代深层次学习系统的性能在很大程度上取决于输入对象的质量。 例如,对于模糊或腐蚀的投入而言,面部识别质量会较低。 但是,很难预测投入质量对更复杂情景的准确性的影响。 我们提出一种深层次的计量学习方法,以便能够直接估计不确定性,而几乎没有额外的计算成本。 发达的 \ textit{Secalface}算法使用可培训的尺度值,以改变嵌入空间的相似性。 这些依赖投入的尺度值代表了对识别结果的信任度,从而允许进行不确定性的估算。 我们对面部识别任务进行了全面实验,显示Sace Face相对于其他不确定性识别面部识别方法的优异性表现。 我们还将结果推广到文字到图像检索任务,表明拟议的方法以显著的幅度击败了竞争者。