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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
時間 2021-01-02
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引言 對於分類問題而言,一個常用的增加訓練樣本的方法是將訓練樣本隨機移動一個小的位移,或者,等價的,在圖像中隨機取一些大的圖像塊。然後以這些圖像塊爲輸入訓練分類模型。在測試階段,可以採用滑窗的方法對每一個圖像塊進行分類,然後組合這些分類結果,得到一個置信度更高的類別標籤。這種技巧被廣泛運用於機器學習算法中,例如:瑞士一個研究組的文章:Multi-column Deep Neural Network
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相關文章
1.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
2.
【OverFeat】《OverFeat:Integrated Recognition, Localization and Detection using Convolutional Networks》
3.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks 論文筆記
4.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks總結
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對 OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks 一文的理解...
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OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks論文閱讀筆記
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