Classifiers trained with class-imbalanced data are known to perform poorly on
test data of the "minor" classes, of which we have insufficient training data.
In this paper, we investigate learning a ConvNet classifier under such a
scenario. We found that a ConvNet significantly over-fits the minor classes,
which is quite opposite to traditional machine learning algorithms that often
under-fit minor classes. We conducted a series of analysis and discovered the
feature deviation phenomenon -- the learned ConvNet generates deviated features
between the training and test data of minor classes -- which explains how
over-fitting happens. To compensate for the effect of feature deviation which
pushes test data toward low decision value regions, we propose to incorporate
class-dependent temperatures (CDT) in training a ConvNet. CDT simulates feature
deviation in the training phase, forcing the ConvNet to enlarge the decision
values for minor-class data so that it can overcome real feature deviation in
the test phase. We validate our approach on benchmark datasets and achieve
promising performance. We hope that our insights can inspire new ways of
thinking in resolving class-imbalanced deep learning.

本文研究了在类别不平衡数据情况下学习卷积神经网络分类器，发现卷积神经网络容易过拟合少数类，提出了一种类别依赖温度的训练方法以解决特征偏移问题，并在基准数据集上验证了有效性。