Adaptive Loss

Adaptive loss functions dynamically adjust the weighting of different loss components during training, aiming to improve model performance and robustness by addressing issues like class imbalance, noisy labels, and uncertainty in predictions. Current research focuses on developing novel weighting schemes, often incorporating uncertainty estimation or task-specific considerations, and applying these to various architectures including convolutional neural networks, transformers, and multi-task learning frameworks. This research is significant because it enhances the accuracy and reliability of machine learning models across diverse applications, from medical image analysis and autonomous driving to natural language processing and recommendation systems.