Existing open set classifiers distinguish between known and unknown inputs by measuring distance in a network's logit space, assuming that known inputs cluster closer to the training data than unknown inputs. However, this approach is typically applied post-hoc to networks trained with cross-entropy loss, which neither guarantees nor encourages the hoped-for clustering behaviour. To overcome this limitation, we introduce Class Anchor Clustering (CAC) loss. CAC is an entirely distance-based loss that explicitly encourages training data to form tight clusters around class-dependent anchor points in the logit space. We show that an open set classifier trained with CAC loss outperforms all state-of-the-art techniques on the challenging TinyImageNet dataset, achieving a 2.4% performance increase in AUROC. In addition, our approach outperforms other state-of-the-art distance-based approaches on a number of further relevant datasets. We will make the code for CAC publicly available.

本文介绍了一种基于距离的损失函数——Class Anchor Clustering (CAC) loss，可明确地训练已知类别在对数空间形成紧密聚类，从而增强网络模型的开集识别表现，在六个标准基准数据集上取得了最先进的表现，并且不会影响分类准确性。

类别锚点聚类: 一种基于距离的开放集识别损失函数