Gradient Smoothing

Gradient smoothing techniques aim to refine the noisy gradients produced during neural network training and inference, improving model performance and efficiency. Current research focuses on applying gradient smoothing in diverse applications, including image processing (e.g., super-resolution, fusion, and target detection), graph neural networks (particularly dynamic graphs), and stochastic optimization, often employing novel algorithms and architectures to achieve this. These advancements enhance model robustness, accelerate training, and improve the interpretability of neural network outputs, impacting various fields from medical imaging to edge AI.