Pareto-Based Many-Objective Convolutional Neural Networks

摘要

Deep convolutional neural networks have been widely used in many areas. Generally, a vast amount of data are required for deep neural networks training, since they have a large number of parameters. This paper devotes to develop a many-objective convolutional neural network (MaO-CNN) model, which can obtain better classification performance than a single-objective one without sufficient training data. The main contributions of this paper are listed as follows: firstly, we propose many-class detection error trade-off (MaDET) and develop a MaO-CNN model in MaDET space; secondly, a hybrid framework of many-objective evolutionary algorithm is proposed for MaO-CNN model training; thirdly, a encoding method is designed for parameters encoding and MaO-CNN evolving. Experimental results based on well-known MNIST and SVHN datasets show that the new proposed model can obtain better results than a conventional one with the same amount of training data.