Multi-path Convolutional Neural Networks for Complex Image Classification

Convolutional Neural Network demonstrates high performance on ImageNet Large-Scale Visual Recognition Challenges contest. Nevertheless, the publish results only show overall performance for all images classes. There is no further analysis for what special images get worse results and how they could be improved. In this paper, we provide deep performance analysis base on different types of images and point out the weaknesses of convolutional neural network through experiment. We design a novel multiple paths convolutional neural network, which feed different versions of images into separated paths to learn more comprehensive features. This model has better presentation for image than traditional single path model. We acquire better classification results on complex validation set on both top 1 and top 5 scores than the best ILSVRC 2013 classification model.