Contents
- 1 What are generative models and discriminative models?
- 2 What is a generative model explain how a discriminative model differentiates a real model from a fake model with an example?
- 3 What is the difference between discriminative and generative machine learning?
- 4 How is a Gan different from a generative model?
What are generative models and discriminative models?
Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model focuses on explaining how the data was generated, while a discriminative model focuses on predicting the labels of the data.
What is a generative model explain how a discriminative model differentiates a real model from a fake model with an example?
A generative model could generate new photos of animals that look like real animals, while a discriminative model could tell a dog from a cat. Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels. Discriminative models capture the conditional probability p(Y | X).
How are generative and discriminative models used?
Generative models can generate new data instances. Discriminative models discriminate between different kinds of data instances. A generative model could generate new photos of animals that look like real animals, while a discriminative model could tell a dog from a cat. GANs are just one kind of generative model.
Which is an example of a generative model?
Examples of generative models are: Discriminative model refers to a class of models used in statistical classification, especially in supervised machine learning. Also known as conditional models, generative modeling learns the boundary between classes or labels in a dataset.
What is the difference between discriminative and generative machine learning?
In maths, discriminative machine learning trains a model which is done by learning model parameters that maximize the conditional probability P (Y|X). On the other side, generative machine learning trains a model to learn parameters, maximizing the joint probability of P (X, Y).
How is a Gan different from a generative model?
In contrast, the generative model tries to produce convincing 1’s and 0’s by generating digits that fall close to their real counterparts in the data space. It has to model the distribution throughout the data space. GANs offer an effective way to train such rich models to resemble a real distribution.