  This paper presents a simulator-assisted training method (SimVAE) for
variational autoencoders (VAE) that leads to a disentangled and interpretable
latent space. Training SimVAE is a two-step process in which first a deep
generator network(decoder) is trained to approximate the simulator. During this
step, the simulator acts as the data source or as a teacher network. Then an
inference network (encoder)is trained to invert the decoder. As such, upon
complete training, the encoder represents an approximately inverted simulator.
By decoupling the training of the encoder and decoder we bypass some of the
difficulties that arise in training generative models such as VAEs and
generative adversarial networks (GANs). We show applications of our approach in
a variety of domains such as circuit design, graphics de-rendering and other
natural science problems that involve inference via simulation.
