ASRL:A robust loss function with potential for development
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Abstract (arXiv)
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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References · 5
- 2015Fast R-CNNcited by 2,933
- 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
- 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
- 2021Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Functioncited by 7
- 2023OWAdapt: An adaptive loss function for deep learning using OWA operatorscited by 2
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