【論文閱讀】AlexNet:ImageNet Classification with Deep Covolutional Network
網絡結構 使用ReLU非線性層 作者在網絡結構中使用了ReLU作爲非線性激活層來代替當時比較常用的tanh或sigmoid，加速了網絡的訓練速度，對此作者也做了對比實驗並可視化了結果。 可以看到同樣是下降到0.25的loss rate，ReLU（實現）比tanh（虛線）少用了六倍的時間。 局部響應歸一化（LRN） 由於ReLU不像tanh或sigmoid一樣，輸出有一定範圍，所以需要對其輸出做歸一
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- 1. 【論文閱讀】Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
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