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Home All issues Volume 674 (June 2023) A&A, 674 (2023) A126 Full HTML
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Table 2

Architecture of spatial attention part in TSA Module with upsampling implemented by bilinear interpolation.

Layer Type Kernel size Stride Input shape Output shape
L1,1 Conv 1 × 1 × 32 1 N × N × 96 N× N× 32
L2,1 Max pooling 3 × 3 × 32 2 N × N × 32 N/2 × N/2 × 32
L2,2 Avg pooling 3 × 3 × 32 2 N × N × 32 N/2 × N/2 × 32
L2,3 Concatenate _ _ N/2 × N/2 × 32 N/2 × N/2 × 64
L2,4 Conv+LeakyReLU 1 × 1 × 32 1 N/2 × N/2 × 64 N/2 × N/2 × 32
L2,5 Conv+LeakyReLU 1 × 1 × 32 1 N/2 × N/2 × 32 N/2 × N/2 × 32
L3,1 Max pooling 3 × 3 × 32 2 N/2 × N/2 × 32 N/4 × N/4 × 32
L3,2 Avg pooling 3 × 3 × 32 2 N/2 × N/2 × 32 N/4 × N/4 × 32
L3,3 Concatenate _ _ N/4 × N/4 × 32 N/4 × N/4 × 64
L3,4 Conv+LeakyReLU 3 × 3 × 32 1 N/4 × N/4 × 64 N/4 × N/4 × 32
L3,5 Conv+LeakyReLU 3 × 3 × 32 1 N/4 × N/4 × 32 N/4 × N/4 × 32
L4,1 Upsampling _ _ N/4 × N/4 × 32 N/2 × N/2 × 32
L4,2 Conv+LeakyReLU 3 × 3 × 32 1 N/2 × N/2 × 32 N/2 × N/2 × 32
L4,3 Add _ _ N/2 × N/2 × 32 N/2 × N/2 × 32
L4,4 Conv+LeakyReLU 1 × 1 × 32 1 N/2 × N/2 × 32 N/2 × N/2 × 32
L5,1 Upsampling _ _ N/2 × N/2 × 32 N× N× 32
L5,2 Conv+Sigmoid 3 × 3 × 32 1 N × N × 32 N× N× 32
L5,3 Conv+LeakyReLU 1 × 1 × 32 1 N × N × 32 N× N× 32
L5,4 Conv 1 × 1 × 32 1 N × N × 32 N× N× 32
L5,5 Multiply _ _ N × N × 32 N× N× 32
L5,6 Add _ _ N × N × 32 N× N× 32

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