Orthogonal Gradient

Orthogonal gradient methods aim to improve machine learning models by modifying training processes to encourage parameter updates that are independent of, or orthogonal to, existing learned features. Current research focuses on applying this principle to diverse areas, including denoising and destriping hyperspectral images, boosting the interpretability of rule ensembles, enhancing the diversity of mixture-of-experts models, and improving the generalization of neural networks for tabular data. These techniques offer potential for increased model efficiency, robustness, and interpretability across various applications, impacting fields ranging from image processing and reinforcement learning to natural language processing.