What is difference between a generative and discriminative model?

What is difference between a generative and discriminative model?

In simple words, a discriminative model makes predictions on the unseen data based on conditional probability and can be used either for classification or regression problem statements. On the contrary, a generative model focuses on the distribution of a dataset to return a probability for a given example.

Is a CNN generative or discriminative?

The convolutional neural networks (CNNs) have proven to be a powerful tool for discriminative learning. Recently researchers have also started to show interest in the generative aspects of CNNs in order to gain a deeper understanding of what they have learned and how to further improve them.

What is discriminative deep learning?

Discriminative Models A Discriminative model ‌models the decision boundary between the classes and learns the conditional probability distribution p(y|x) [1]. Some examples of discriminative models are logistic regression, SVMs, ANN, KNN and Conditional Random Fields.

Are neural networks discriminative?

A discriminative model is so called because it tries to learn which values x will map to y, so it tries to discriminate among the inputs. Neural networks are an example. A discriminative model is given a more precise task, just try to predict y given x, so it’s typically supervised.

Is Perceptron a discriminative model?

Commonly used discriminative learning algorithms include Support-vector machines, logistic regression, and decision trees; ensemble methods, i.e., random forest and gradient boosting; multi-layer perceptrons and, the top-most layers of deep neural networks.

Why do we use a discriminative classifier?

Discriminative Classifiers learn what the features in the input are most useful to distinguish between the various possible classes. Mathematically, it directly calculates the posterior probability P(y|x) or learn a direct map from input x to label y. So, these models try to learn the decision boundary for the model.

What is a discriminative feature?

We characterize each thing as a discriminative feature descriptor including static features (e.g., content-based features) and easily integrated dynamic features (e.g., locations, and instantaneous status of things).

What is discriminative representation?

A discriminative representation is proposed by discovering key information of the input data. • The representation is parameterized with hidden variables and can be learned from training data. • Human action is recognized by combining the parameterized representation and discriminative classifier.