Directly Connected Convolutional Neural Networks

Convolutional neural networks (CNNs) have better performance in feature extraction and classification. Most of the applications are based on a traditional structure of CNNs. However, due to the fixed structure, it may not be effective for large dataset which will spend much time for training. So, we use a new algorithm to optimize CNNs, called directly connected convolutional neural networks (DCCNNs). In DCCNNs, the down-sampling layer can directly connect the output layer with three-dimensional matrix operation, without full connection (i.e., matrix vectorization). Thus, DCCNNs have less weights and neurons than CNNs. We conduct the comparison experiments on five image databases: MNIST, COIL-20, AR, Extended Yale B, and ORL. The experiments show that the model has better recognition accuracy and faster convergence than CNNs. Furthermore, two applications (i.e., water quality evaluation and image classification) following the proposed concepts further confirm the generality and capability of DCCNNs.