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About the application:

From spade's project page : We propose spatially-adaptive normalization, a easy but effective layer for synthesizing photorealistic photos given an input semantic layout. Previous ways directly feed the semantic layout as input to the network, which is then processed through stacks of convolution, normalization, and nonlinearity layers. We present that this is suboptimal because the normalization layers tend to wash away semantic info. To address the problem, we propose using the input layout for modulating the activations in normalization layers through a spatially-adaptive, learned transformation. Experiments on a few challenging datasets demonstrate the advantage of the proposed way compared to existing approaches, regarding both visual fidelity and alignment with input layouts. Finally, our model allows players to easily control the style and content of synthesis results as well as make multi-modal results.