Densely Connected Convolutional Networks
Abstract 最近的研究表明，如果卷積網絡在靠近輸入和接近輸出的層之間包含較短的連接，那麼卷積網絡可以更深入、更準確並有效地訓練。 本文引入了緊密卷積網絡（densenet），它以一種前饋的方式將每一層連接到另一層。 傳統的具有L層的卷積網絡在每一層和其後續層之間都有L連接，而我們的網絡有L(L+1)/2個直接連接。對於每個圖層，前面所有圖層的特徵圖都用作輸入，其自身的特徵圖也用作後面所有圖層
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- 1. Densely Connected Convolutional Networks
- 2. DenseNet--Densely Connected Convolutional Networks
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- 4. DenseNet——Densely Connected Convolutional Networks
- 5. Densely Connected Convolutional Networks(DenseNet)
- 6. DenseNet---《Densely Connected Convolutional Networks》
- 7. 【DenseNet】《Densely Connected Convolutional Networks》
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- 9. Densely Connected Convolutional Networks翻譯
- 10. Densely Connected Convolutional Networks (DenseNet 2017 CVPR)
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相關文章
- 1. Densely Connected Convolutional Networks
- 2. DenseNet--Densely Connected Convolutional Networks
- 3. Densely Connected Convolutional Networks(2017)
- 4. DenseNet——Densely Connected Convolutional Networks
- 5. Densely Connected Convolutional Networks(DenseNet)
- 6. DenseNet---《Densely Connected Convolutional Networks》
- 7. 【DenseNet】《Densely Connected Convolutional Networks》
- 8. DenseNet Densely Connected Convolutional Networks
- 9. Densely Connected Convolutional Networks翻譯
- 10. Densely Connected Convolutional Networks (DenseNet 2017 CVPR)