Lai Loss: A Novel Loss for Gradient Control

TL;DR

Lai Loss introduces a new loss function that incorporates gradient regularization directly into the loss, enabling better control over model smoothness and sensitivity, which improves generalization and noise resistance.

Contribution

The paper proposes Lai Loss, a novel loss function that integrates gradient regularization geometrically, offering a new way to control model properties during training.

Findings

Lai Loss effectively controls model smoothness and sensitivity.

It maintains stable performance while enhancing noise resistance.

Preliminary experiments show promising results on Kaggle datasets.

Abstract

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.