Loss Gradient

Loss gradients, the rate of change of a loss function with respect to model parameters, are central to training machine learning models. Current research focuses on mitigating challenges arising from loss gradients, such as conflicting gradients in multi-task learning and imbalanced gradients in long-tailed datasets, often employing techniques like gradient surgery, dynamic weighting, and alternate training procedures across various architectures including convolutional neural networks and transformers. These efforts aim to improve model training efficiency, generalization, and robustness, impacting diverse applications from image segmentation and object detection to physics-informed neural networks and energy-efficient deep learning.