【时间】2019.08.08
【题目】Inception v1,v2,v3,v4论文汇总。
[v1] Going Deeper with Convolutions, 6.67% test error, http://arxiv.org/abs/1409.4842
[v2] Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 4.8% test error, http://arxiv.org/abs/1502.03167
[v3] Rethinking the Inception Architecture for Computer Vision, 3.5% test error, http://arxiv.org/abs/1512.00567
[v4] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, 3.08% test error, http://arxiv.org/abs/1602.07261
一些分析见:
2、大话CNN经典模型:GoogLeNet(从Inception v1到v4的演进)



本文汇总了Inceptionv1至v4的论文链接及测试误差,分别介绍Google团队如何通过创新架构降低图像分类错误率,从6.67%降至3.08%。包含GoogLeNet的发展历程与Inception模块的迭代改进。
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