新损失函数提升分类准确率，且不增加计算开销。
Enhancing Cross Entropy with a Linearly Adaptive Loss Function for Optimized Classification Performance
- 在交叉熵基础上加可调项，根据真实类预测概率动态调整梯度。
- 在CIFAR-100上使用ResNet，准确率持续优于标准交叉熵。
- 保持原有效率，适合想改进损失函数的研究者。
我们提出了线性自适应交叉熵损失函数，这是一种基于信息论的新型度量方法。与标准交叉熵损失相比，该方法引入了一个依赖于真实类别预测概率的附加项，从而优化了一类独热编码标签下的分类任务。在基于ResNet的模型上使用CIFAR-100数据集进行评估，初步结果表明，该方法在分类准确率上持续优于标准交叉熵损失函数。同时，该方法保持了与传统交叉熵相近的计算效率。这些发现表明，该方法为未来损失函数设计提供了新的思路。
原文摘要 · Abstract (English)
We propose the Linearly Adaptive Cross Entropy Loss function. This is a novel measure derived from the information theory. In comparison to the standard cross entropy loss function, the proposed one has an additional term that depends on the predicted probability of the true class. This feature serves to enhance the optimization process in classification tasks involving one-hot encoded class labels. The proposed one has been evaluated on a ResNet-based model using the CIFAR-100 dataset. Preliminary results show that the proposed one consistently outperforms the standard cross entropy loss function in terms of classification accuracy. Moreover, the proposed one maintains simplicity, achieving practically the same efficiency to the traditional cross entropy loss. These findings suggest that our approach could broaden the scope for future research into loss function design.
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