Orthogonal Convolution

Orthogonal convolutions are a specialized type of convolutional operation in neural networks designed to maintain orthogonality in weight matrices, leading to improved efficiency and stability during training. Current research focuses on incorporating orthogonal convolutions into various architectures, such as RawNet for audio processing and text-to-image diffusion models, often alongside techniques like separable convolutions and temporal convolutional networks to enhance performance and reduce computational cost. This approach offers benefits in areas like adversarial robustness, lightweight model design, and improved accuracy in tasks such as acoustic scene classification and fake audio detection, demonstrating its practical value across diverse applications.