1.视频教程：
B站、网易云课堂、腾讯课堂
2.代码地址：
Gitee
Github
3.存储地址：
Google云
百度云：
提取码：
为 什 么 要 用 小 卷 积 核 替 代 大 卷 积 核 ? 为什么要用小卷积核替代大卷积核? 为什么要用小卷积核替代大卷积核?
大卷积核：
优点:感受域范围大
举例:AlexNet、LeNet等网络都使用了比较大的卷积核，如5×5,11×11
缺点:参数量多;计算量大
小卷积核
优点:参数量少;计算量小;整合三个非线性激活层代替单一非线性激活层，增加模型判别能力
举例:VGG之后
缺点:感受域不足;深度堆叠卷积容易出现不可控的因素
《Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network》
—待写
作者：Chao Peng,etc
单位：清华大学&旷视科技
发表会议及时间：CVPR 2017
Submission history
From: Chao Peng [view email]
[v1] Wed, 8 Mar 2017 06:14:55 UTC (2,502 KB)
https://arxiv.org/abs/1703.02719
- Abstract
One of recent trends [30, 31, 14] in network architec- ture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more ef- ficient than a large kernel, given the same computational complexity. However, in the field of semantic segmenta- tion, where we need to perform dense per-pixel prediction, we find that the large kernel (and effective receptive field) plays an important role when we have to perform the clas- sification and localization tasks simultaneously. Following our design principle, we propose a Global Convolutional Network to address both the classification and localization issues for the semantic segmentation. We also suggest a residual-based boundary refinement to further refine the ob- ject boundaries. Our approach achieves state-of-art perfor- mance on two public benchmarks and significantly outper- forms previous results, 82.2% (vs 80.2%) on PASCAL VOC 2012 dataset and 76.9% (vs 71.8%) on Cityscapes dataset.
一 论文导读
二 论文精读
三 代码实现
