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Table 2 Detailled architecture of the low resolution GAN \(0\rightarrow 32^{3}\). \(d=64\)

From: Cosmological N-body simulations: a challenge for scalable generative models

OperationParameter sizeOutput Shape
Input \(z\,\mathcal{N}(0,1)\) (n,256)
Reshape (n,4,4,4,4d)
TrConv 3D (Sride 2)(4,4,4,4d,4d)(n,8,8,8,4d)
LReLu (α = 0.2) (n,16,16,16,4d)
TrConv 3D (Sride 2)(4,4,4,4d,2d)(n,16,16,16,2d)
LReLu (α = 0.2) (n,16,16,16,2d)
TrConv 3D (Sride 2)(4,4,4,d,d)(n,32,32,32,d)
LReLu (α = 0.2) (n,32,32,32,2d)
TrConv 3D (Sride 1)(4,4,4,d,d)(n,32,32,32,d)
LReLu (α = 0.2) (n,32,32,32,2d)
TrConv 3D (Sride 1)(4,4,4,d,1)(n,32,32,32,1)
Input generated image (n,32,32,32,1)
Conv 3D (Sride 2)(4,4,4,1,d)(n,32,32,32,d)
LReLu (α = 0.2) (n,32,32,32,d)
Conv 3D (Sride 2)(4,4,4,d,d)(n,32,32,32,d)
LReLu (α = 0.2) (n,32,32,32,d)
Conv 3D (Sride 1)(4,4,4,d,2d)(n,16,16,16,2d)
LReLu (α = 0.2) (n,16,16,16,2d)
Conv 3D (Sride 1)(4,4,4,2d,4d)(n,8,8,8,4d)
LReLu (α = 0.2) (n,8,8,8,4d)
Conv 3D (Sride 1)(4,4,4,4d,8d)(n,4,4,4,8d)
LReLu (α = 0.2) (n,4,4,4,8d)
Reshape (n,512d)