the discriminator which tries to guess the context. The resulting loss is then (inversely) backpropagated through the encoder. Miscellaneous applications GAN can be used to detect glaucomatous images helping the early diagnosis which is essential to avoid partial or total loss of vision. GANs that produce photorealistic images can be used to visualize interior design, industrial design, shoes, bags, and clothing items or items for computer games' scenes. Such networks were reported to be used by Facebook. GANs have been used to create forensic facial reconstructions of deceased historical figures. GANs can reconstruct 3D models of objects from images, generate novel objects as 3D point clouds, and model patterns of motion in video. GANs can be used to age face photographs to show how an individual's appearance might change with age. GANs can also be used to inpaint missing features in maps, transfer map styles in cartography or augment street view imagery. Relevance feedback on GANs can be used to generate images and replace image search systems. A variation of the GANs is used in training a network to generate optimal control inputs to nonlinear dynamical systems. Where the discriminatory network is known as a critic that checks the optimality of the solution and the generative network is known as an Adaptive network that generates the optimal control. The critic and adaptive network train each other to approximate a nonlinear optimal control. GANs have been used to visualize the effect that climate change will have on specific houses. A GAN model called Speech2Face can reconstruct an image of a person's face after listening to their voice. In 2016 GANs were used to generate new molecules for a variety of protein targets implicated in cancer, inflammation, and fibrosis. In 2019 GAN-generated molecules were validated experimentally all the way into mice. Whereas the majority of GAN applications are in image processing, the work has also been done with time-series data. For example, recurrent GANs (R-GANs) have been used to generate energy data for machine learning. History The most direct inspiration for GANs was noise-contrastive estimation, which uses the same loss function as GANs and which Goodfellow studied during his PhD in 2010–2014. Other people had similar ideas but did not develop them similarly. An idea involving adversarial networks was published in a 2010 blog post by Olli Niemitalo. This idea was never implemented and did not involve stochasticity in the generator and