《Inception V3-Rethinking the Inception Architecture for Computer Vision》论文笔记
1. 论文思想 在其它条件都满足的（数据充足且足够好）的情况下，增加模型的尺寸以及计算量会带来实质上的优势，但是可供计算的资源总是有限的，特别是在移动设备上，并不能无节制的增加模型的尺寸。例如，在VggNet模型中使用的参数量是AlexNet参数量的三倍，实际取得的效果也是好于AlexNet的。在之前的Google-Net中采用了优化之后的Inception-V1结构以及去掉全连接层等方式使得模型
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相关文章
- 1. Inception V3 -- 《Rethinking the Inception Architecture for Computer Vision》论文笔记
- 2. Rethinking the Inception Architecture for Computer Vision
- 3. Inception Architecture for Computer Vision
- 4. Inception系列2_Rethinking the Inception Architecture for Computer Vision
- 5. Rethinking the Inception Architecture for Computer Vision-Inception-V3
- 6. 论文笔记——Rethinking the Inception Architecture for Computer Vision
- 7. Inception-v2和Inception-v3来源论文《Rethinking the Inception Architecture for Computer Vision》读后总结
- 8. 【InceptionnetV3】Rethinking the Inception Architecture for Computer Vision摘要
- 9. GoogleNetV3 Rethinking the Inception Architecture for Computer Vision
- 10. Inception v3:Rethinking the Inception Architecture for Computer Vision翻译理解