GANs — Generative Adversarial Networks
A discriminative model learns features from the input data and uses the information about them to determine boundaries in observed data and use those (statistical boundaries) to make predictions like the kind of regression or classification.
But a generative model uses the features learned from the training data to create more data out of the random noise supplied to it.
It does not require labels and hence is unsupervised.
The model is generative because the sample it creates is absolutely unique. The sample does not exist in the training data, but is very similar to it. Also, a generative model is probabilistic. The sample it creates is unique, but it is different for every input. In other words, a generative model incorporates a kind of randomness- which leads to new entities with different inputs.
A discriminative model, on the other hand, is deterministic. For the same input, its output is always the same.
Generative models are used for up-sampling an imbalanced dataset, missing value imputation, and anonymizing sensitive…
