ResNet：Deep Residual for Image Recognition
1 abstract 将层改为residual functions，模型更容易优化 模型越深越好，是VGG的8倍。模型复杂度比VGG更低 是2015年ImageNet 的1st 深度是获胜的关键 2 indroduction 模型的深度丰富了模型的features 深模型的问题：梯度消失或爆炸，难找到最优值，现有方案：normalized initlalization ,intermediate
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相关文章
- 1. ResNet--Deep Residual Learning for Image Recognition
- 2. [ResNet]Deep Residual Learning for Image Recognition
- 3. Deep Residual Learning for Image Recognition
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- 6. ResNet(deep residual learning for image recognition)
- 7. ResNet——Deep Residual Learning for Image Recognition
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- 9. Deep Residual Learning for Image Recognition 笔记
- 10. Deep Residual Learning for Image Recognition总结