FCCNs: Fully Complex-valued Convolutional Networks using Complex-valued Color Model and Loss Function

Although complex-valued convolutional neural networks (iCNNs) have existed for a while, they lack proper complex-valued image inputs and loss functions. In addition, all their operations are not complex-valued as they have both complex-valued convolutional layers and real-valued fully-connected layers. As a result, they lack an end-to-end flow of complex-valued information, making them inconsistent w.r.t. the claimed operating domain, i.e., complex numbers. Considering these inconsistencies, we propose a complex-valued color model and loss function and turn fully-connected layers into convolutional layers. All these contributions culminate in what we call FCCNs (Fully Complex-valued Convolutional Networks), which take complex-valued images as inputs, perform only complex-valued operations, and have a complex-valued loss function. Thus, our proposed FCCNs have an end-to-end flow of complex-valued information, which lacks in existing iCNNs. Our extensive experiments on five image classification benchmark datasets show that FCCNs consistently perform better than existing iCNNs. Code is available at https://github.com/saurabhya/FCCNs.