Deep learners tend to perform well when trained under the closed set
assumption but struggle when deployed under open set conditions. This motivates
the field of Open Set Recognition in which we seek to give deep learners the
ability to recognize whether a data sample belongs to the known classes trained
on or comes from the surrounding infinite world. Existing open set recognition
methods typically rely upon a single function for the dual task of
distinguishing between knowns and unknowns as well as making known class
distinction. This dual process leaves performance on the table as the function
is not specialized for either task. In this work, we introduce Cascading
Unknown Detection with Known Classification (Cas-DC), where we instead learn
specialized functions in a cascading fashion for both known/unknown detection
and fine class classification amongst the world of knowns. Our experiments and
analysis demonstrate that Cas-DC handily outperforms modern methods in open set
recognition when compared using AUROC scores and correct classification rate at
various true positive rates.

深度学习模型在封闭集假设下表现良好，但在开放集条件下部署时遇到困难。本文提出了一种级联未知检测与已知分类方法（Cas-DC），通过学习专门用于已知 / 未知检测和已知类别分类的级联函数，实现在开放集识别任务中优于现代方法的性能表现。