resnet18参考文献

ResNet18是由Kaiming He等人于2016年提出的一种深度残差网络，用于解决深度神经网络中的退化问题。该网络结构通过引入残差连接（residual connections）来解决梯度消失和梯度爆炸的问题，从而能够训练更深的网络。

以下是关于ResNet18的参考文献：

1. 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 (CVPR) (pp. 770-778).

2. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. In European conference on computer vision (ECCV) (pp. 630-645).

3. Kaiming He's homepage: https://www.cs.cmu.edu/~kaiminghe/

相关问题

resnet的参考文献

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).

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Identity mappings in deep residual networks. In European conference on computer vision (pp. 630-645).

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (pp. 1026-1034).

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2015). Convolutional Neural Networks with Low-rank Regularization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5967-5975).

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2015). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 346-361).

有关于ResNet的参考文献

ResNet，全称为残差网络（Residual Network），是一种深度卷积神经网络架构，特别设计用于解决深层网络训练过程中的梯度消失问题，使得网络可以更深、更有效率地学习特征。其核心思想是引入了“残差块”（Residual Block），可以直接将输入添加到经过几个层处理后的信号上，简化了模型的学习过程。

经典的ResNet论文发表在2015年的《Deep Residual Learning for Image Recognition》(CVPR) 上，由Kaiming He等人撰写，该文章因其显著提高了图像识别性能而广受关注。这篇论文的主要引用信息如下：

标题：Deep Residual Learning for Image Recognition
作者：Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
会议/期刊：Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
年份：2016
链接：https://arxiv.org/abs/1512.03385

如果你需要深入研究或了解ResNet，以下是一些相关的参考文献：
1. "Identity Mappings in Deep Residual Networks" by He et al., ECCV 2016 (深入讨论残差模块)
2. "PyTorch官方文档关于ResNet的教程": https://pytorch.org/tutorials/beginner/resnet_tutorial.html
3. "CS231n: Convolutional Neural Networks for Visual Recognition" - 斯坦福大学课程讲义，包含ResNet在内的深度学习详细讲解。