# Multi-path Convolutional Neural Networks for Complex Image Classification

arXiv: https://arxiv.org/abs/1506.04701 · HTML: https://mlsanity.com/paper/1506.04701/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2015.

## Built on by (most cited first, 4 of 4)

- [Pruning for Feature-Preserving Circuits in CNNs](https://mlsanity.com/paper/2206.01627/) (2022)
- [Balancing Accuracy and Latency in Multipath Neural Networks](https://mlsanity.com/paper/2104.12040/) (2021)
- [End-To-End Data-Dependent Routing in Multi-Path Neural Networks](https://mlsanity.com/paper/2107.02450/) (2021)
- [Multi-path Convolutional Neural Networks Efficiently Improve Feature Extraction in Continuous Adventitious Lung Sound Detection](https://mlsanity.com/paper/2107.04226/) (2021)

## Abstract (arXiv)

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.