Class Balanced Loss for Image Classification

In the study of image classification, neural network learning relies heavily on datasets. Due to variability in the difficulty of collecting images in reality, datasets tend to have class imbalance problems, which undoubtedly increases the difficulty of classification. During the training of a neural network, classes with a large number of images are naturally trained more often than classes with a small number of images. Because of imbalanced training, the classification ability of neural networks on test and validation sets differs greatly in different categories. The test results of more training classes are better, and the test results of classes with less training are poor. In this paper, we propose two kinds of balanced loss functions, namely, CEFL loss and CEFL2 loss, by rebalancing the cross-entropy loss function and focal loss function. The experimental results show that the proposed loss functions are significantly able to improve classification accuracy on class-imbalanced datasets.