Generative Modeling Techniques

What are common tasks which a machine learning algorithm can solve? List includes Classification, Regression, Clustering, Data Imputation, Outlier Analysis, Regressive modelling, Machine Translation and the list is endless. There is something common in all the tasks mentioned earlier. They are building boundaries around the data points that you share to achieve a goal. But are rarely about to generate something similar that you fed to them.

Lets assume an example of a classifier, which has been trained to classify animal in the image as Dog or Cat. Say if the model focus on 10 features out of each image and one feature represent a distinctive information such as all dogs wear a collar while none of the cats wear one. Then most of the machine learning model will just focus on this discriminative feature which sets apart dogs from cats. And this particular approach of machine Learning is what known as Discriminative Modelling which focuses on predicting the labels of the data.

Contrary to them is another approach of modelling known as Generative Modelling which try to model, how the data is placed through out the space. That is it not only they learn the distinctive property of cats and dogs but learn that cats have curved ears & hairs around mouth while dogs have pointed ears and sharp bulging out face features. A generative model focuses on explaining how the data…