Neural networks have become proficient in many areas from image and voice recognition, to the understanding of natural language. Over the past years their accuracy has improved to a level almost comparable to humans. There are, however, still many tasks that neural networks are unable to carry out - think about human creativity for example. Training a machine to compose a piece of music or paint a picture requires a different set of training skills. Back in 2014, Ian Goodfellow first introduced Generative Adversarial Networks (GANs) which are able to build on their training in an unsupervised manner, analysing past mistakes or short fallings and building on them to improve the results. The GANs are built of two components, the generator neural network (the trainee), and the discriminator neural network (the trainer).
Topics: Neural Networks