Preserving Loss

Preserving Loss in machine learning focuses on designing loss functions that maintain crucial information during model training, preventing the loss of important features or structures present in the input data. Current research emphasizes incorporating prior knowledge, such as physical laws or feature importance, into loss functions, often utilizing neural networks (including autoencoders, transformers, and physics-informed neural networks) to achieve this. This approach improves model accuracy, robustness (e.g., against adversarial attacks), and the interpretability of results across diverse applications, including image processing, speech enhancement, and physical system modeling. The resulting improvements in model performance and reliability have significant implications for various scientific fields and practical applications.