xce在卷积神经网络是什么

在卷积神经网络中，Xception（eXtreme version of Inception）是一种深度卷积神经网络。Xception网络基于Inception网络的思想，但有一些改进，其最主要的改进是采用了“深度可分离卷积（Depthwise Separable Convolution）”。

深度可分离卷积将标准卷积操作分成了两个部分：深度卷积和逐点卷积。深度卷积只考虑每个输入通道上的滤波器，逐点卷积将不同通道的深度卷积结果相加。深度可分离卷积相比于标准卷积，可以显著减少计算量和参数数量。

Xception网络采用深度可分离卷积代替了Inception网络中的标准卷积，使得网络更加轻便，同时减少了过拟合的风险。在ImageNet分类任务上，Xception网络取得了当时最好的结果。

相关问题

卷积神经网络图像分类的参考文献

根据提供的引用内容，卷积神经网络是完成图像识别任务的最佳算法之一。以下是一些卷积神经网络图像分类的参考文献：
1. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
2. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
4. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
5. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-First AAAI Conference on Artificial Intelligence.

有哪些关于卷积神经网络研究现状的参考文献

以下是关于卷积神经网络研究现状的一些参考文献：

1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

2. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).

3. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

4. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

5. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).

6. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

7. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440).

8. Yu, F., Koltun, V., & Funkhouser, T. (2017). Dilated residual networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 472-480).

9. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848.

请注意，以上的参考文献只是给您一些常见的卷积神经网络研究现状，而不是所有的研究现状。如果您想要更详细的信息，请参考相关的学术论文或著作。