  Recently, deep learning models have achieved great success in computer vision
applications, relying on large-scale class-balanced datasets. However,
imbalanced class distributions still limit the wide applicability of these
models due to degradation in performance. To solve this problem, in this paper,
we concentrate on the study of cross entropy which mostly ignores output scores
on incorrect classes. This work discovers that neutralizing predicted
probabilities on incorrect classes improves the prediction accuracy for
imbalanced image classification. This paper proposes a simple but effective
loss named complement cross entropy based on this finding. The proposed loss
makes the ground truth class overwhelm the other classes in terms of softmax
probability, by neutralizing probabilities of incorrect classes, without
additional training procedures. Along with it, this loss facilitates the models
to learn key information especially from samples on minority classes. It
ensures more accurate and robust classification results on imbalanced
distributions. Extensive experiments on imbalanced datasets demonstrate the
effectiveness of the proposed method.
