# woosual/multiModalityFusionForClassification

<https://repo.masters-masters.com/en/repos/woosual/multiModalityFusionForClassification>

[Home](https://repo.masters-masters.com/en) › [woosual](https://repo.masters-masters.com/en/owners/woosual) › [multiModalityFusionForClassification](https://repo.masters-masters.com/en/repos/woosual/multiModalityFusionForClassification)

woosual/multiModalityFusionForClassification has 103 stars on GitHub. Its RepoMasters card: Prototype tier. 多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分…

| Field | Value |
| --- | --- |
| Owner | [woosual](https://repo.masters-masters.com/en/owners/woosual) |
| Stars | 103 |
| Forks | 20 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | BSD-2-Clause |
| Created | 2020 |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [woosual/multiModalityFusionForClassification](https://github.com/woosual/multiModalityFusionForClassification) |

## Repository

多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.