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

本研究延伸了梯度基于 Class Activation Mapping (CAM) 方法，以适用于二分类器，并可视化二元面部属性分类器的活跃区域。实验结果表明，偏斜分类器主要依赖于多数类别的偏倚激活，而均衡分类器在不平衡数据上经过调整后，多数和少数类别都能显示出预期的激活情况。

有偏的二元属性分类器忽略了大多数类别

Biased Binary Attribute Classifiers Ignore the Majority Classes

Existing semi-supervised learning (SSL) algorithms typically assume
class-balanced datasets, although the class distributions of many real-world
datasets are imbalanced. In general, classifiers trained on a class-imbalanced
dataset are biased toward the majority classes. This issue becomes more
problematic for SSL algorithms because they utilize the biased prediction of
unlabeled data for training. However, traditional class-imbalanced learning
techniques, which are designed for labeled data, cannot be readily combined
with SSL algorithms. We propose a scalable class-imbalanced SSL algorithm that
can effectively use unlabeled data, while mitigating class imbalance by
introducing an auxiliary balanced classifier (ABC) of a single layer, which is
attached to a representation layer of an existing SSL algorithm. The ABC is
trained with a class-balanced loss of a minibatch, while using high-quality
representations learned from all data points in the minibatch using the
backbone SSL algorithm to avoid overfitting and information loss.Moreover, we
use consistency regularization, a recent SSL technique for utilizing unlabeled
data in a modified way, to train the ABC to be balanced among the classes by
selecting unlabeled data with the same probability for each class. The proposed
algorithm achieves state-of-the-art performance in various class-imbalanced SSL
experiments using four benchmark datasets.

本文提出了一种可扩展的类不平衡半监督学习算法，它可以有效地利用未标记的数据，同时通过引入单层的辅助平衡分类器（ABC）来缓解类别不平衡问题，并使用一种新的半监督学习技术（一致性正则化）去平衡分类器，从而在四个基准数据集上实现了最先进的性能。