The deep neural network architecture, called a denoising autoencoder, is similar to FlowNet and U-Net and consists of encoder and decoder components to progressively subsample and upsample inputs ...

Instead of learning this denoising step explicitly, we draw an analogy from the denoising autoencoder and model the joint distribution instead.