FCN:Fully Convlutional Networks for semantic segmentation要點
FCN首先將深度學習引入到語義分割領域，網絡的要點總結如下： 1. 將全連接層變爲卷積，好處： ①傳統的分類網絡，比如LeNet、AlexNet等，只接受固定尺寸的輸入併產生非空間輸出，原因在於全連接層參數的限制，而且這些網絡在通過全連接層把輸入展開成向量的時候丟失了圖片原有的空間信息。以VGG16和PASCAL數據集爲例，網絡去除了最後的分類層，並將所有的全連接層轉化爲上述的卷積層，然後添加了一
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相關文章
- 1. FCN：Fully convolutional networks for semantic segmentation
- 2. FCN—— Fully Convolutional Networks for Semantic Segmentation
- 3. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 4. Fully Convolutional Networks for Semantic Segmentation
- 5. FCN-Fully Convolutional Networks for Semantic Segmentation
- 6. 【Semantic segmentation】Fully Convolutional Networks for Semantic Segmentation 論文解析
- 7. 深度估計 Fully Convolutional Networks for Semantic Segmentation
- 8. ICCV2019-Asymmetric Non-local Neural Networks for Semantic Segmentation
- 9. 讀書筆記《Fully Convolutional Networks for Semantic Segmentation》
- 10. 【論文筆記】Fully Convolutional Networks for Semantic Segmentation