Split-Complex Convolutional Neural Networks

Beginning with the seminal work of [1], the last half-decade of artificial intelligence and computer vision has been dominated by the stunning success of convolutional neural networks (CNNs). In visual recognition, a robust classifier must be able to recognize objects under deformation. One solution that has been proposed for improving invariance under rotation is complex-valued CNNs [2, 3]. While the results for complex-valued CNNs have been intriguing, questions remain open about whether the successes of complex neural networks arises from the link between complex numbers and rotational transformations, the increased complexity of the complex-valued computational graph, or simply the inclusion of additional network parameters.