Gradient Rectification

Gradient rectification techniques aim to improve the performance of deep learning models by addressing issues stemming from suboptimal gradient distributions. Current research focuses on enhancing gradient information in various domains, including spatial and frequency domains, often employing specialized modules within encoder-decoder architectures or by strategically supplying context between independently trained network segments. These methods show promise in improving the accuracy and efficiency of tasks such as depth map super-resolution, low-light image enhancement, and visual place retrieval, ultimately leading to more robust and effective computer vision systems.