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Semantic Image Synthesis with Spatially-
Adaptive Normalization
Taesung Park1∗ Ming-Yu Liu2 Ting-Chun Wang2 Jun-Yan Zhu2,3...
Motivation
• The conventional network architecture, which is built by stacking
convolutional, normalization, and nonlinear...
1. Replace the segmentation mask m with the image class label and
making the modulation parameters spatially-invariant——
C...
Why does SPADE work better?
• It can better preserve semantic information against common
normalization layers
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization
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Semantic Image Synthesis with Spatially-Adaptive Normalization

  1. 1. Semantic Image Synthesis with Spatially- Adaptive Normalization Taesung Park1∗ Ming-Yu Liu2 Ting-Chun Wang2 Jun-Yan Zhu2,3 1UC Berkeley 2NVIDIA 3MIT CSAIL https://nvlabs.github.io/SPADE/
  2. 2. Motivation • The conventional network architecture, which is built by stacking convolutional, normalization, and nonlinearity layers, is at best sub- optimal, because their normalization layers tend to “wash away” information in input semantic masks.
  3. 3. 1. Replace the segmentation mask m with the image class label and making the modulation parameters spatially-invariant—— Conditional Batch Normalization Layer 2. Replace the segmentation mask with another image, making the modulation parameters spatially invariant and setting N = 1 —— AdaIN
  4. 4. Why does SPADE work better? • It can better preserve semantic information against common normalization layers

yutian lin

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