Transposed convolution
Transposed Convolution理解 轉置卷積（反捲積）多用於上採樣（UP Sample），可以將特徵圖尺寸由H*W*C變爲2H*2W*C，達到擴大特徵圖的目的。用一句話來解釋： 反捲積是一種特殊的正向卷積，先按照一定的比例通過補0來擴大輸入圖像的尺寸，接着旋轉卷積核，再進行正向卷積。 一種錯誤的理解：轉置卷積就是卷積的逆（Deconvolution）。實際上，**卷積是不可能求逆運算
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- 1. Deconvolution/Transposed convolution
- 2. UpPooling/UpSampling/Deconvolution/Transposed convolution
- 3. Up-sampling with Transposed Convolution
- 4. 反捲積（Transposed Convolution, Fractionally Strided Convolution or Deconvolution）
- 5. 反捲積(Transposed Convolution, Fractionally Strided Convolution or Deconvolution)
- 6. Transposed convolution & Fully Convolutional Neural Network
- 7. 【深度學習】轉置卷積（Transposed Convolution）
- 8. 反捲積(Transposed Convolution)詳細推導
- 9. 反捲積(Deconvolution)|轉置卷積(Transposed Convolution)
- 10. 一文搞懂 deconvolution、transposed convolution、sub-pixel or fractional convolution
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相關文章
- 1. Deconvolution/Transposed convolution
- 2. UpPooling/UpSampling/Deconvolution/Transposed convolution
- 3. Up-sampling with Transposed Convolution
- 4. 反捲積（Transposed Convolution, Fractionally Strided Convolution or Deconvolution）
- 5. 反捲積(Transposed Convolution, Fractionally Strided Convolution or Deconvolution)
- 6. Transposed convolution & Fully Convolutional Neural Network
- 7. 【深度學習】轉置卷積（Transposed Convolution）
- 8. 反捲積(Transposed Convolution)詳細推導
- 9. 反捲積(Deconvolution)|轉置卷積(Transposed Convolution)
- 10. 一文搞懂 deconvolution、transposed convolution、sub-pixel or fractional convolution