  Convolutional Neural Networks demonstrate high performance on ImageNet
Large-Scale Visual Recognition Challenges contest. Nevertheless, the published
results only show the overall performance for all image classes. There is no
further analysis why certain images get worse results and how they could be
improved. In this paper, we provide deep performance analysis based on
different types of images and point out the weaknesses of convolutional neural
networks through experiment. We design a novel multiple paths convolutional
neural network, which feeds different versions of images into separated paths
to learn more comprehensive features. This model has better presentation for
image than the 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.
