Generative Adversarial Networks (GANs): - Type of deep neural network architecture that uses unsupervised machine learning - Made up by generator and a discriminator network. Both networks train each other, while simultaneously trying to outwit each other. Generator network - Generate new data from a randomly generated vector of numbers, called a latent space. Discriminator network - Tries to differentiate between the real data and the data generated. - It can either perform multi-class classification or binary classification. Important concepts related to GANs - Important measure quality of the models use divergence (KL divergence, JS divergence...). - Nash equilibrium, which is a state that we try to achieve during training. - Objective functions: To measure the similarity. - Scoring algorithms: Calculating the accuracy of a GAN is simple. Some scoring algorithms: some scoring algorithms, some scoring algorithms, Mode Score... Problems with training GANs: - Mode collapse: gen
Think big, start small, move fast.