An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN
Architectures
An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN
Architectures
Orthogonal convolutional layers are the workhorse of multiple areas in machine learning, such as adversarial robustness, normalizing flows, GANs, and Lipschitzconstrained models. Their ability to preserve norms and ensure stable gradient propagation makes them valuable for a large range of problems. Despite their promise, the deployment of orthogonal convolution in …