Self-paced hybrid dilated convolutional neural networks

Convolutional neural networks (CNNs) can learn the features of samples by supervised manner, and obtain outstanding achievements in many application fields. In order to improve the performance and generalization of CNNs, we propose a self-learning hybrid dilated convolution neural network (SPHDCNN), which can choose relatively reliable samples according to the current learning ability during training. In order to avoid the loss of useful feature map information caused by pooling, we introduce hybrid dilated convolution. In the proposed SPHDCNN, weight is applied to each sample to reflect the easiness of the sample. SPHDCNN employs easier samples for training first, and then adds more difficulty samples gradually according to the current learning ability. It gradually improves its performance by this learning mechanism. Experimental results show SPHDCNN has strong generalization ability, and it achieves more advanced performance compared to the baseline method.