ResNet：Deep Residual for Image Recognition
1 abstract 將層改爲residual functions，模型更容易優化 模型越深越好，是VGG的8倍。模型複雜度比VGG更低 是2015年ImageNet 的1st 深度是獲勝的關鍵 2 indroduction 模型的深度豐富了模型的features 深模型的問題：梯度消失或爆炸，難找到最優值，現有方案：normalized initlalization ,intermediate
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- 1. ResNet--Deep Residual Learning for Image Recognition
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- 4. ResNet —— Deep Residual Learning for Image Recognition
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- 7. ResNet——Deep Residual Learning for Image Recognition
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相關文章
- 1. ResNet--Deep Residual Learning for Image Recognition
- 2. [ResNet]Deep Residual Learning for Image Recognition
- 3. Deep Residual Learning for Image Recognition
- 4. ResNet —— Deep Residual Learning for Image Recognition
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- 7. ResNet——Deep Residual Learning for Image Recognition
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- 9. Deep Residual Learning for Image Recognition 筆記
- 10. Deep Residual Learning for Image Recognition總結