【论文阅读】SegNet A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
看完摘要习惯性先去看整体框架图，如下图，在卷积结构中看到了Batch Normalization，因此首先去了解了一下什么是Batch Normalization。 推荐博客：http://www.cnblogs.com/guoyaohua/p/8724433.html，看完后基本都懂了。在这里我简单总结一下： BN的目的是解决“Internal Covariate Shift”问题，主要作用是防
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相关文章
- 1. 论文阅读笔记：《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》
- 2. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation----2016 SegNet论文解读
- 3. 【SegNet】 A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 4. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 5. 【阅读笔记】《SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation》
- 6. 论文笔记：SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 7. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation 视频语义分割
- 8. 论文阅读：ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
- 9. TPAMI2017 SegNet：A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation
- 10. 语义分割-- SegNet：A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation