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Recently, deep learning models have achieved great success in computer visionapplications, relying on large-scale class-balanced datasets. However,imbalanced class distributions still limit the wide applicability of thesemodels due to degradation in performance. To solve this problem, in this paper,we concentrate on the study of cross entropy which mostly ignores output scoreson incorrect classes. This work discovers that neutralizing predictedprobabilities on incorrect classes improves the prediction accuracy forimbalanced image classification. This paper proposes a simple but effectiveloss named complement cross entropy based on this finding. The proposed lossmakes the ground truth class overwhelm the other classes in terms of softmaxprobability, by neutralizing probabilities of incorrect classes, withoutadditional training procedures. Along with it, this loss facilitates the modelsto learn key information especially from samples on minority classes. Itensures more accurate and robust classification results on imbalanceddistributions. Extensive experiments on imbalanced datasets demonstrate theeffectiveness of the proposed method.

Imbalanced Image Classification with Complement Cross Entropy