ImageNet classification with deep convolutional neural networks

Krizhevsky, Alex, Sutskever, Ilya, Hinton, Geoffrey E.·2017·Communications of the ACM

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  84. Deep forest
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  90. Zero-Shot Learning by Convex Combination of Semantic Embeddings
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