这篇论文教你怎么不用标数据就自动设好验证阈值，在四个数据集上测了准确率94%，跟用标数据的方法差不多，挺实用的。
本论文针对孪生验证网络（Siamese verification networks）中距离阈值的设定问题，提出了一种基于双峰分布假设的无监督方法。该方法通过识别距离分布两个模态之间的最低点来确定阈值，无需标注数据。在MNIST、CIFAR-10、LFW和PKLot四个数据集上的实验显示，平均验证准确率达到94%，与需要标注的等错误率（EER）方法性能相当。该工作使得验证阈值可直接在部署环境中更新，降低了人工标注成本。
How Far is Too Far? Defining the Distance Threshold for Verification Siamese Networks
Siamese verification networks are widely used to compare items such as faces, cars, or signatures. In these scenarios, the network is trained to learn an embedding space in which similar objects are mapped closer together, while dissimilar objects are mapped further apart. Two objects are considered to belong to the same class (e.g., the same person in two different images) when the distance between their embeddings falls below a predefined threshold. Defining this threshold, however, is a non-trivial task and typically requires labeled data. In this work, we assume that the distribution of distances produced by a siamese verification network can be approximated by a bimodal function. Based on this assumption, we propose an unsupervised method to determine the verification threshold by identifying the minimum point between the two modes. The proposed approach does not require annotated samples, enabling the verification threshold to be updated directly in the deployment environment without the cost of manual labeling. We evaluate our method on four datasets: MNIST, CIFAR-10, LFW, and PKLot. The results indicate that the proposed approach achieves an average verification accuracy of 94%, comparable to the Equal Error Rate method, while eliminating the need for labeled data.