Orthogonality Regularization

Orthogonality regularization is a technique used to improve the performance and robustness of neural networks by encouraging independence between different learned features or parameters. Current research focuses on applying this technique within various architectures, including vision transformers, convolutional neural networks, and graph neural networks, often in conjunction with parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) or Givens rotations to enhance model efficiency and generalization. This approach addresses issues like overfitting, feature redundancy, and dimensional collapse, leading to improved performance on tasks such as domain generalization, object detection, and speech processing. The resulting models exhibit increased robustness and better disentanglement of learned representations, impacting diverse fields from computer vision to audio analysis.