提出一种分段自适应损失函数，提升模型鲁棒性与适用范围。
ASRL:A robust loss function with potential for development
- 采用分段设计与自适应参数调整提升鲁棒性
- 在五个不同数据集上均优于现有损失函数
- 适合需要高稳定性回归任务的研究者使用
本文提出一种基于已有鲁棒损失函数的分段式鲁棒损失函数。该函数通过分段设计与自适应参数调整，实现了高鲁棒性与广泛适用性。通过将其应用于回归问题，并在五个不同维度、样本量及领域数据集上与多种损失函数对比，实验结果验证了其优势。多组实验表明，该损失函数在不同场景下均表现出更优性能，具备良好的应用前景与发展潜力。
原文摘要 · Abstract (English)
In this article, we proposed a partition:wise robust loss function based on the previous robust loss function. The characteristics of this loss function are that it achieves high robustness and a wide range of applicability through partition-wise design and adaptive parameter adjustment. Finally, the advantages and development potential of this loss function were verified by applying this loss function to the regression question and using five different datasets (with different dimensions, different sample numbers, and different fields) to compare with the other loss functions. The results of multiple experiments have proven the advantages of our loss function .
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