Convolutional Fully-Connected Capsule Network (CFC-CapsNet): A Novel and Fast Capsule Network
Capsule Networks (CapsNets) suffer from performance degradation, high computational overhead, and excessive parameter counts in complex image classification tasks. To address these limitations, this paper proposes CFC-CapsNet, which introduces a novel Convolutional–Fully Connected (CFC) capsule layer. This hybrid layer replaces conventional dense capsule structures with fewer yet more discriminative capsules, significantly compressing model size while preserving hierarchical spatial modeling capability. By integrating vectorized feature representations with dynamic routing, the method enhances feature representation efficiency. Extensive experiments on CIFAR-10, SVHN, and Fashion-MNIST demonstrate that CFC-CapsNet achieves average accuracy gains of 1.2–2.8% over baseline CapsNets, accelerates training and inference by 2.3×, and reduces parameter count by 37–51%. The proposed architecture thus achieves a favorable trade-off among accuracy, computational efficiency, and model compactness.