Optimizing Deep Learning Models with Hybrid Nonlinear Loss Functions: Integrating Heterogeneous Nonlinear Properties for Enhanced Performance

Current deep learning models underperform when using loss functions characterized by single properties.Therefore, optimizing these models with a combination of multiple attributes is essential to enhance performance.We propose a novel hybrid nonlinear loss function technique incorporating heterogeneous nonlinear properties to achieve optimal performance.We evaluated the proposed method using six analytical techniques: contour map visualization, loss error frequency analysis, scatter plot visualization, loss function visualization, gradient descent analysis, and covariate Analysis with convex and linear coefficients.Our experiments on semantic segmentation tasks using the Pascal visual object classes and automatic target recognition datasets demonstrated superior optimization performance compared to existing loss functions.