L2-Net: Deep Learning of Discriminative Patch Descriptor in Euclidean Space(2017)(一)
本文主要貢獻： 1、提出了一種新的採樣策略，使網絡在少數的epoch迭代中，接觸百萬量級的訓練樣本； 2、基於局部圖像塊匹配問題，強調度量描述子的相對距離； 3、在中間特徵圖上加入額外的監督； 4、描述符的緊實性。 基於CNN的局部圖像塊匹配方法可以分爲兩類：一是，作爲二分類問題，不存在明確的特徵描述子概念，好處是準確率相對第二類高很多，但可移植性能差；二是，CNN輸出學習
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相關文章
- 1. Application of deep learning in Industrial area
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- 4. Deep Learning in R
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