将多分类置信度校准转化为二分类问题，提升校准效果。
Confidence Calibration of Classifiers with Many Classes
- 把多分类校准转为单一二分类代理任务，简化流程。
- 在图像与文本分类模型上显著改善现有校准方法性能。
- 适合需要高置信度准确性的多类别分类场景。
基于神经网络的分类模型常以最大预测概率作为置信度评分，但该值往往无法准确反映正确预测的概率，需后处理校准。然而，许多校准方法在类别数量多时表现不佳。为此，本文将多分类器校准问题转化为单个代理二分类器的校准问题，使标准校准方法能更高效地应用。我们在多个用于图像或文本分类的神经网络上评估该方法，结果表明其显著提升了现有校准技术的性能。
原文摘要 · Abstract (English)
For classification models based on neural networks, the maximum predicted class probability is often used as a confidence score. This score rarely predicts well the probability of making a correct prediction and requires a post-processing calibration step. However, many confidence calibration methods fail for problems with many classes. To address this issue, we transform the problem of calibrating a multiclass classifier into calibrating a single surrogate binary classifier. This approach allows for more efficient use of standard calibration methods. We evaluate our approach on numerous neural networks used for image or text classification and show that it significantly enhances existing calibration methods.
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