  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.
