To visualize the regions of interest that classifiers base their decisions on, different Class Activation Mapping (CAM) methods have been developed. However, all of these techniques target categorical classifiers only, though most real-world tasks are binary classification. In this paper, we extend gradient-based CAM techniques to work with binary classifiers and visualize the active regions for binary facial attribute classifiers. When training an unbalanced binary classifier on an imbalanced dataset, it is well-known that the majority class, i.e. the class with many training samples, is mostly predicted much better than minority class with few training instances. In our experiments on the CelebA dataset, we verify these results, when training an unbalanced classifier to extract 40 facial attributes simultaneously. One would expect that the biased classifier has learned to extract features mainly for the majority classes and that the proportional energy of the activations mainly reside in certain specific regions of the image where the attribute is located. However, we find very little regular activation for samples of majority classes, while the active regions for minority classes seem mostly reasonable and overlap with our expectations. These results suggest that biased classifiers mainly rely on bias activation for majority classes. When training a balanced classifier on the imbalanced data by employing attribute-specific class weights, majority and minority classes are classified similarly well and show expected activations for almost all attributes

翻译：为了可视化分类器决策所依赖的感兴趣区域，已有多种类激活映射（Class Activation Mapping, CAM）方法被开发出来。然而，这些技术仅适用于分类别分类器，尽管大多数实际任务为二元分类。本文中，我们将基于梯度的CAM技术扩展至二元分类器，并可视化二元面部属性分类器的活跃区域。众所周知，在不平衡数据集上训练非平衡二元分类器时，多数类（即训练样本较多的类别）的预测准确率通常远高于少数类（训练样本较少的类别）。在CelebA数据集上的实验中，我们验证了这一结果——训练一个非平衡分类器以同时提取40种面部属性时，结果符合预期。通常认为，偏向性分类器已学会主要针对多数类提取特征，且激活能量的比例主要集中于图像中属性所在的特定区域。然而，我们发现多数类样本的规则激活极少，而少数类的活跃区域似乎较为合理，且与预期重叠。这些结果表明，偏向性分类器主要依赖偏置激活来处理多数类。当采用属性特定的类别权重在不平衡数据上训练平衡分类器时，多数类和少数类的分类效果相近，并且几乎所有属性均展现出预期的激活模式。