Multi-path Convolutional Neural Networks for Complex Image Classification

2015/06/15 by Mingming Wang, Wang, Mingming
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.1506.04701

openalex publication_date 2015/06/15 · arxiv created 2015/06/22 · arxiv updated 2015/06/23 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Improving neural networks by preventing co-adaptation of feature detectors
• Backpropagation Applied to Handwritten Zip Code Recognition

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