  Training of deep neural networks heavily depends on the data distribution. In
particular, the networks easily suffer from class imbalance. The trained
networks would recognize the frequent classes better than the infrequent
classes. To resolve this problem, existing approaches typically propose novel
loss functions to obtain better feature embedding. In this paper, we argue that
drawing a better decision boundary is as important as learning better features.
Inspired by observations, we investigate how the class imbalance affects the
decision boundary and deteriorates the performance. We also investigate the
feature distributional discrepancy between training and test time. As a result,
we propose a novel, yet simple method for class imbalanced learning. Despite
its simplicity, our method shows outstanding performance. In particular, the
experimental results show that we can significantly improve the network by
scaling the weight vectors, even without additional training process.
