Lai Loss: A Novel Loss for Gradient Control
This addresses gradient control for machine learning practitioners, but appears incremental as it builds on existing regularization methods.
The paper tackles the problem of controlling model gradients for better generalization and noise resistance by introducing the Lai loss, which integrates gradient regularization directly into the loss function using geometric concepts. Preliminary experiments on Kaggle datasets show it can control model smoothness and sensitivity while maintaining stable performance.
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.