No abstract

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, we focus on the study of cross entropy: it mostly ignores output scores on wrong classes. In this work, we discover that neutralizing predicted probabilities on incorrect classes helps improve accuracy of prediction for imbalanced image classification. This paper proposes a simple but effective loss named complement cross entropy (CCE) based on this finding. Our 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 for imbalanced class distributions. Extensive experiments on imbalanced datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods.