Generative adversarial networks

Generative adversarial networks (GANs) are a class of artificial intelligence algorithms used in unsupervised machine learning, implemented by a system of two neural networks contesting with each other in a zero-sum game framework. They were introduced by Ian Goodfellow et al. in 2014.[1]

This technique can generate photographs that look authentic to human observers. For example, a synthetic photograph of a cat that fools the discriminator into accepting it as an actual photograph.[2]

Method

One network generates candidates and one evaluates them.[1] Typically, the generative network learns to map from a latent space to a particular data distribution of interest, while the discriminative network discriminates between instances from the true data distribution and candidates 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).[1][3]

In practice, a known dataset serves as the initial training data for the discriminator. Training the discriminator involves presenting it with samples from the dataset, until it reaches some level of accuracy. Typically the generator is seeded with a randomized input that is sampled from a predefined latent space (e.g. a multivariate normal distribution). Thereafter, samples synthesized by the generator are evaluated by the discriminator. Backpropagation is applied in both networks so that the generator produces better images, while the discriminator becomes more skilled at flagging synthetic images. [4] The generator is typically a deconvolutional neural network, and the discriminator is a convolutional neural network.

The idea to infer models in a competitive setting (model versus discriminator) was proposed by Li, Gauci and Gross in 2013.[5] Their method is used for behavioral inference. It is termed Turing Learning,[6] as the setting is akin to that of a Turing test.

Application

GANs have been 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.[7] Recently, GANs have modeled patterns of motion in video.[8] They have also been used to reconstruct 3D models of objects from images[9] and to improve astronomical images.[10]

References

  1. 1 2 3 Goodfellow, Ian J.; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). "Generative Adversarial Networks". arXiv:1406.2661 Freely accessible [stat.ML].
  2. ↑ Salimans, Tim; Goodfellow, Ian; Zaremba, Wojciech; Cheung, Vicki; Radford, Alec; Chen, Xi (2016). "Improved Techniques for Training GANs". arXiv:1606.03498 Freely accessible [cs.LG].
  3. ↑ Luc, Pauline; Couprie, Camille; Chintala, Soumith; Verbeek, Jakob (2016-11-25). "Semantic Segmentation using Adversarial Networks". NIPS Workshop on Adversarial Training, Dec , Barcelona, Spain. 2016. arXiv:1611.08408 Freely accessible.
  4. ↑ Andrej Karpathy, Pieter Abbeel, Greg Brockman, Peter Chen, Vicki Cheung, Rocky Duan, Ian Goodfellow, Durk Kingma, Jonathan Ho, Rein Houthooft, Tim Salimans, John Schulman, Ilya Sutskever, And Wojciech Zaremba, Generative Models, OpenAI, retrieved April 7, 2016
  5. ↑ Li, Wei; Gauci, Melvin; Gross, Roderich (July 6, 2013). "A Coevolutionary Approach to Learn Animal Behavior Through Controlled Interaction". Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO 2013). Amsterdam, The Netherlands: ACM. pp. 223–230.
  6. ↑ Li, Wei; Gauci, Melvin; Groß, Roderich (30 August 2016). "Turing learning: a metric-free approach to inferring behavior and its application to swarms". Swarm Intelligence. 10 (3): 211–243. doi:10.1007/s11721-016-0126-1.
  7. ↑ Greenemeier, Larry (June 20, 2016). "When Will Computers Have Common Sense? Ask Facebook". Scientific American. Retrieved July 31, 2016.
  8. ↑ "Generating Videos with Scene Dynamics". web.mit.edu.
  9. ↑ "3D Generative Adversarial Network". 3dgan.csail.mit.edu.
  10. ↑ Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan (2017-02-01). "Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit". arXiv:1702.00403 Freely accessible [astro-ph.IM].


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