Hybrid Loss

Hybrid loss functions are increasingly used in machine learning to improve model performance by combining multiple loss components, each addressing specific aspects of a problem. Current research focuses on applications across diverse fields, including image segmentation, object detection, and trajectory optimization, often incorporating architectures like UNets, transformers, and Siamese networks. The strategic combination of loss functions allows for a more nuanced optimization process, leading to improved accuracy, robustness, and the ability to handle challenges like class imbalance and constraint violations in various applications. This approach holds significant promise for advancing numerous fields by enhancing the capabilities of deep learning models.