VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
本文参考网上多个对论文的理解. 1.VGG 重要贡献： 展示出网络的深度是算法优良性能的关键部分 2.VGG网络的改进点： a.使用了更小的3*3卷积核，和更深的网络。两个3*3卷积核的堆叠相对于5*5卷积核的视野，三个3*3卷积核的堆叠相当于7*7卷积核的视野。这样一方面可以有更少的参数；另一方面拥有更多的非线性变换，增加了CNN对特征的学习能力。 b.在VGGNet的卷积结构中，引入1*1的卷
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相关文章
- 1. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 2. VGG——Very deep convolutional networks for large-scale image recognition
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- 4. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 5. Very Deep Convolutional Networks for Large-Scale Image Recognition
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- 9. 盏茶论文CV篇(二) Very Deep Convolutional Networks for Large-Scale Image Recognition
- 10. [VGG论文详解](2014)Very Deep Convolutional Networks For Large-Scale Image Recognition