GAN: Generating New Images with CNN

Generally, a neural network needs labeled examples to learn effectively. Unsupervised learning approaches to learn from unlabeled data have not worked very well. A generative adversarial network, or simply a GAN, is part of an unsupervised learning approach but based on differentiable generator networks. GANs were first invented by Ian Goodfellow and others in 2014. Since then they have become extremely popular. This is based on game theory and has two players or networks: a generator network and b) a discriminator network, both competing against each other. This dual network game theory-based approach vastly improved the process of learning from unlabeled data. The generator network produces fake data and passes it to a discriminator. The discriminator network also sees real data and predicts whether the data it receives is fake or...