首页> 外文OA文献 >Table 8: Accuracy of k-NN classifier on DBAP features derived from pre-trained AlexNet with DBAP layer. The classification results achieved are better than those obtained by MaxPool features derived from regular AlexNet. The inclusion of DBAP layer also improves the discrimination quality of FC features in AlexNet with DBAP layer.

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Table 8: Accuracy of k-NN classifier on DBAP features derived from pre-trained AlexNet with DBAP layer. The classification results achieved are better than those obtained by MaxPool features derived from regular AlexNet. The inclusion of DBAP layer also improves the discrimination quality of FC features in AlexNet with DBAP layer.

机译：表8：k-nn分类器的准确性DBAP功能从预先培训的alexNet与DBAP层派生的。所实现的分类结果优于来自常规亚历尼网的MaxPool特征获得的分类结果。包含DBAP层还可以提高与DBAP层的亚历尼网FC功能的辨别质量。

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3. Table 6: Accuracy of SVM classifier on DBAP features derived from pre-trained AlexNet with DBAP layer. The classification results are better than the results obtained by MaxPool features derived from a regular AlexNet. The inclusion of DBAP layer also shows better FC features from the model giving better classification results in comparison to the FC features from regular AlexNet model. [O] . -1

机译：表6：使用DBAP层的预先培训的AlexNet派生的DBAP功能上SVM分类器的精度。分类结果优于来自常规AlexNet的MaxPool功能获得的结果。包含DBAP层还显示了从常规AlexNet模型的FC特征比较的模型中显示出更好的FC特征，从而提供更好的分类导致FC功能。

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