Decoding the Science Behind Generative Adversarial Networks

Generative adversarial networks(GANs) took the Machine Learning field by storm last year with those impressive fake human-like faces. Bonus Point* They are basically generated from nothing. Irrefutably, GANs implements implicit learning methods where the model learns without the data directly passing through the network, unlike those explicit techniques where weights are learned directly from the data. Okay, suppose in the city of Rio de Janeiro, money forging felonies are increasing so a department is appointed to check in these cases. Detectives are expected to classify the legit ones and fake ones.