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Variational autoencoder architecture

MH Max Highsmith
JC Jianlin Cheng
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The VAE component of VEHiCLE utilizes two neural networks for the encoding and decoding components, where the encoder is trained for the parameters of q0 and the decoder is trained to optimize the parameters of p0. The VEHiCLE encoder network contains 7 convolutional layers with kernel counts: 32, 64, 128, 256, 256, 512, 512. Each convolutional layer is separated by leaky ReLU and batch normalization. The decoder network has 7 layers of convolution transpose with the kernel counts 512, 512, 256, 256, 128, 64, 32, also separated by leaky ReLU and batch norm functions. The decoder network is appended by a Sigmoid activation function placing outputs in the range of [0,1].

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