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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.07884 |
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| _version_ | 1866916720896114688 |
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| author | Lai, YuFei |
| author_facet | Lai, YuFei |
| contents | In the field of machine learning, traditional regularization methods tend to directly add regularization terms to the loss function. This paper introduces the "Lai loss", a novel loss design that integrates the regularization terms (specifically, gradients) into the traditional loss function through straightforward geometric concepts. This design penalizes the gradients with the loss itself, allowing for control of the gradients while ensuring maximum accuracy. With this loss, we can effectively control the model's smoothness and sensitivity, potentially offering the dual benefits of improving the model's generalization performance and enhancing its noise resistance on specific features. Additionally, we proposed a training method that successfully addresses the challenges in practical applications. We conducted preliminary experiments using publicly available datasets from Kaggle, demonstrating that the design of Lai loss can control the model's smoothness and sensitivity while maintaining stable model performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07884 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lai Loss: A Novel Loss for Gradient Control Lai, YuFei Machine Learning In the field of machine learning, traditional regularization methods tend to directly add regularization terms to the loss function. This paper introduces the "Lai loss", a novel loss design that integrates the regularization terms (specifically, gradients) into the traditional loss function through straightforward geometric concepts. This design penalizes the gradients with the loss itself, allowing for control of the gradients while ensuring maximum accuracy. With this loss, we can effectively control the model's smoothness and sensitivity, potentially offering the dual benefits of improving the model's generalization performance and enhancing its noise resistance on specific features. Additionally, we proposed a training method that successfully addresses the challenges in practical applications. We conducted preliminary experiments using publicly available datasets from Kaggle, demonstrating that the design of Lai loss can control the model's smoothness and sensitivity while maintaining stable model performance. |
| title | Lai Loss: A Novel Loss for Gradient Control |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.07884 |