解决目标域新增未知类时的标签分布偏移问题
Open Set Label Shift with Test Time Out-of-Distribution Reference
- 用源域分类器和类别判别器分三步估计标签分布
- 无需重训练即可将源域模型迁移到目标域
- 适合处理有未知类别的开放集场景
开放集标签偏移（OSLS）指源域与目标域的标签分布发生变化，且目标域包含额外的分布外（OOD）类别。本文利用源域的分布内（ID）分类器和ID/OOD分类器，构建源域与目标域开放集标签分布的估计器。在合理假设下，估计过程分为三个阶段：1）估计源域中OOD类的标签分布；2）通过EM算法进行最大似然估计（MLE），获得目标域标签分布；3）在放松对OOD分类器假设条件下，估计目标域中OOD类的标签分布。第1和第3阶段的估计误差通过集中不等式量化。该方法可直接校准源域训练的ID分类器至目标域，无需重新训练。在多种开放集标签偏移设置下的实验验证了模型有效性。代码已公开于https://github.com/ChangkunYe/OpenSetLabelShift。
原文摘要 · Abstract (English)
Open set label shift (OSLS) occurs when label distributions change from a source to a target distribution, and the target distribution has an additional out-of-distribution (OOD) class. In this work, we build estimators for both source and target open set label distributions using a source domain in-distribution (ID) classifier and an ID/OOD classifier. With reasonable assumptions on the ID/OOD classifier, the estimators are assembled into a sequence of three stages: 1) an estimate of the source label distribution of the OOD class, 2) an EM algorithm for Maximum Likelihood estimates (MLE) of the target label distribution, and 3) an estimate of the target label distribution of OOD class under relaxed assumptions on the OOD classifier. The sampling errors of estimates in 1) and 3) are quantified with a concentration inequality. The estimation result allows us to correct the ID classifier trained on the source distribution to the target distribution without retraining. Experiments on a variety of open set label shift settings demonstrate the effectiveness of our model. Our code is available at https://github.com/ChangkunYe/OpenSetLabelShift.
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