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Generative Modelling
Generative Modelling do not necessarily require labelled datasets. It can work with unlabelled data to learn the
underlying distribution of the data and can generate structured data from the random noise dataset. So, if
random images are fed as training data for the model it can create relevant output based on the features of the
input data. If there are random images which depict streets, cars, buildings, sky, etc. In a given dataset of street
images, a Generative Modelling can learn to generate new street scenes that look like the ones in the dataset. In
another example, if given a dataset of news articles, a generative model can learn to generate new articles that
resemble the style and content of the training data. Let us take an example.
The following images are given as input to the Generative AI model:
The output produced based on input images by generative AI are as follows:
Introduction to Generative AI 327

