Fully Convolutional Neural Network Structure and Its Loss Function for Image Classification

The overall structure of a convolutional neural network classifier includes multiple convolutional layers and one or more linear layers. Due to the fully connected characteristics of linear layer networks, there are usually many parameters, which may easily lead to local optimization and over-fitting of the model. Therefore, its indispensability in Convolutional Neural Networks (CNNs) classifier is questionable. At the same time, the excessive number of output features of the convolutional layer will also lead to the curse of dimensionality. After verifying the redundancy characteristics of the final activation function, linear layer, and the number of channels by eigen-values analysis of latent features and experiments, we propose a Fully Convolutional Neural Network (FCNN) classifier architecture, which removes the linear layers and the corresponding activation functions from the conventional CNN classifiers. By modifying the number of output channels of the last convolutional layer, the network can be trained directly through Softmax Loss. Furthermore, a softmax-free loss (POD Loss) based on Predefined Optimal-Distribution of latent features is adopted instead of Softmax Loss to obtain better recognition performance. Experiments on multiple commonly used datasets and typical networks have proved that the network structure not only reduces the amount of parameters and calculation, but also improves the recognition rate. In the meanwhile, the adoption of POD Loss further improves the classification accuracy and robustness of the model, making them play a better synergy. Code is available in https://github.com/TianYuZu/Fully-Convolutional-Network.