Title: Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework. Abstract: Enforcing orthogonality in neural networks is an antidote for gradient vanishing/exploding problems, sensitivity to adversarial perturbation, and bounding generalization errors. However, many previous approaches are heuristic, and the orthogonality of convolutional layers is not systematically studied. Some of these designs are not exactly orthogonal, while others only consider standard convolutional layers and propose specific classes of their realizations. We propose a theoretical framework for orthogonal convolutional layers to address this problem, establishing the equivalence between diverse orthogonal convolutional layers in the spatial domain and the paraunitary systems in the spectral domain. Since a complete factorization exists for paraunitary systems, any orthogonal convolution layer can be parameterized as convolutions of spatial filters. Our framework endows high expressive power to various convolutional layers while maintaining their exact orthogonality. Furthermore, our layers are memory and computationally efficient for deep networks compared to previous designs. Our versatile framework, for the first time, enables the study of architecture designs for deep orthogonal networks, such as choices of skip connection, initialization, stride, and dilation. Consequently, we scale up orthogonal networks to deep architectures, including ResNet and ShuffleNet, substantially increasing the performance over their shallower counterparts. Finally, we show how to construct residual flows, a flow-based generative model that requires strict Lipschitzness, using our orthogonal networks.