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.

中文翻译：

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改进的损失分类功能

提出了一种没有抽样程序的改进损失函数，以改善由于样本不足而导致的不良分类。可调参数用于扩大损失范围，最小化易于分类的样本的权重，并进一步替代采样函数，这些函数被添加到交叉熵损失和SoftMax损失中。实验结果表明，在各种网络体系结构和不同数据集上，我们的损失函数的所有分类性能均得到改善。总而言之，与传统的损失函数相比，我们的改进版本不仅提高了分类性能，而且降低了网络训练的难度。