Orthogonal Training

Orthogonal training is a technique used to improve the stability, robustness, and generalization of deep learning models by imposing orthogonality constraints on weight matrices. Current research focuses on applying this method to various architectures, including vision-language models and convolutional neural networks, often incorporating techniques like Cayley parameterization and polar decomposition-based initialization to achieve and maintain orthogonality. This approach shows promise in enhancing model performance in diverse applications, such as image classification, adversarial robustness, and the detection of synthetically generated images, by mitigating issues like vanishing/exploding gradients and overfitting.