Improved Loss Function for Image Classification

Abstract

An improved loss function free of sampling procedures is proposed to improve the ill-performed classification by sample shortage. Adjustable parameters are used to expand the loss scope, minimize the weight of easily classified samples, and further substitute the sampling function, which are added to the cross-entropy loss and the SoftMax loss. Experiment results indicate that improvements in all classification performance of our loss function are shown in various network architectures and on different datasets. To summarize, compared with traditional loss functions, our improved version not only elevates classification performance but also lowers the difficulty of network training.