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【inv2】Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
時間 2021-01-02
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基本情況 這是一篇cv.LG2015的文章,所屬領域爲computer vision. Abstract 在訓練由於前面層的參數的變化,會導致本層輸入分佈的變化,因此導致訓練一個DNN十分複雜,我們不得不降低訓練速度和小心的初始化.這種現象被成爲internal covariate shift. 解決上述問題,通常採用的方法是normalize輸入層.我們的方法力圖使規範化成爲模型架構的一部分,並
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
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