An intuitive introduction to Generative Adversarial Networks (GANs)

In the perfect equilibrium, the generator would capture the general training data distribution. As a result, the discriminator would be always unsure of whether its inputs are real or not. In the DCGAN paper, the authors describe the combination of some deep learning techniques as key for training GANs. These techniques include: (i) the all convolutional net and (ii) Batch Normalization (BN). The first emphasizes strided convolutions (instead of pooling layers) for both: increasing and decreasing feature's spatial dimensions.