Generative adversarial networks

Generative adversarial networks are a branch of unsupervised machine learning, implemented by a system of two neural networks competing against each other in a zero-sum game framework. They were first introduced by Ian Goodfellow et al. in 2014.

This technique can generate photographs that look authentic to human observers.

One network is generative and one is discriminative. Typically, the generative network is taught to map from a latent space to a particular data distribution of interest, and the discriminative network is simultaneously taught to discriminate between instances from the true data distribution and synthesized instances produced by the generator. The generative network's training objective is to increase the error rate of the discriminative network (i.e., "fool" the discriminator network by producing novel synthesized instances that appear to have come from the true data distribution). These models are used for computer vision tasks.

In practice, a particular dataset serves as the training data for the discriminator. Training the discriminator involves presenting the discriminator with samples from the dataset and samples synthesized by the generator, and backpropagating from a binary classification loss. In order to produce a sample, typically the generator is seeded with a randomized input that is sampled from a predefined latent space (e.g., a multivariate normal distribution). Training the generator involves back-propagating the negation of the binary classification loss of the discriminator. The generator adjusts its parameters so that the training data and generated data cannot be distinguished by the discriminator model. The goal is to find a setting of parameters that makes generated data look like the training data to the discriminator network. In practice, the generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network.

GANs can be used to produce samples of photorealistic images for the purposes of visualizing new interior/industrial design, shoes, bags and clothing items or items for computer games' scenes. These networks were reported to be used by Facebook. Recently, GANs have been able to model rudimentary patterns of motion in video. They have also been used to reconstruct 3D models of objects from images.

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