A generative model describes how a dataset is generated, in terms of a probabilistic model. By sampling from this model, we are able to generate new data.
Suppose we have a dataset containing images of horses. We may wish to build a model that can generate a new image of a horse that has never existed but still looks real because the model has learned the general rules that govern the appearance of a horse. This is the kind of problem that can be solved using generative modeling
what must be found in Generative Models ? A generative model must also be probabilistic rather than deterministic. If our model is just a fixed calculation, such as taking the mean value of every pixel in the dataset,then it is not said to be generative because the model produces the same output every time. The model must include a stochastic element that impacts the individual samples generated by the model.
there are some unknown probabilistic distribution that explains why some images are likely to be found in the training dataset and other images are not. It is our job to build a model that mimics this distribution as closely as possible and then sample from it to generate new, distinct observations that look as if they could have been included in the original training set.