Notes: All convolutions and transposed convolutions are with stride 2 and padding 1. Unless stated otherwise, layers have parametric-RELU (⁠$α=0.2$⁠) activation functions, except output layers of the preimage maps, which have sigmoid activation functions (since input data are normalized [0, 1]). Adam and Cayley ADAM optimizers have learning rates $2×10-4$ and $10-4$⁠, respectively. The preimage map/decoder network is always taken as transposed of the feature map/encoder network. $c=48$ for 3D cars; and $c=64$ for all others. Further, $k^=3$ and stride 1 for MNIST, fMNIST, SVHN and 3DShapes; and $k^=4$ for others. SVHN and 3DShapes are resized to $28×28$ input dimensions.