Improving Convolutional Networks with Self-Calibrated Convolutions 自卷積模塊
在2020 cvpr 上面我又看到一篇挺好的文章，這裏分享給大家。這個文章是Improving Convolutional Networks with Self-Calibrated Convolutions。這是一個即插即用的一個模塊，挺好的。這個模塊主要是用來增大網絡的感受野的。另外這個模塊也可以不需要增加太多的參數就可以獲得這個效果。整體還是比較有效的。 整篇 論文 的核心大概就是這幅圖了。
相關文章
- 1. SCNet論文詳解：Improving Convolutional Networks with Self-calibrated Convolutions
- 2. CVPR2020 - Improving Convolutional Networks with Self-Calibrated Convolutions（通過自校準卷積改進卷積神經網絡）
- 3. 理解「卷積」 Understanding Convolutions
- 4. Semi-Supervised Classification with Graph Convolutional Networks
- 5. 圖卷積網絡GRAPH CONVOLUTIONAL NETWORKS
- 6. 圖卷積Graph Convolutional Networks
- 7. Inverting Convolutional Networks with Convolutional Networks 論文理解
- 8. Semi-Supervised Classification With Graph Convolutional Networks
- 9. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
- 10. 5.Deeper Depth Prediction with Fully Convolutional Residual Networks
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相關文章
- 1. SCNet論文詳解：Improving Convolutional Networks with Self-calibrated Convolutions
- 2. CVPR2020 - Improving Convolutional Networks with Self-Calibrated Convolutions（通過自校準卷積改進卷積神經網絡）
- 3. 理解「卷積」 Understanding Convolutions
- 4. Semi-Supervised Classification with Graph Convolutional Networks
- 5. 圖卷積網絡GRAPH CONVOLUTIONAL NETWORKS
- 6. 圖卷積Graph Convolutional Networks
- 7. Inverting Convolutional Networks with Convolutional Networks 論文理解
- 8. Semi-Supervised Classification With Graph Convolutional Networks
- 9. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
- 10. 5.Deeper Depth Prediction with Fully Convolutional Residual Networks