Understanding the Distinction of Generative AI and Discriminative AI 🔍

 

Generative AI

Generative AI models aim to understand and model the underlying distribution of the data they are trained on. These models can generate new data samples that are similar to the training data. Generative models learn the joint probability distribution $P(X, Y)$ of the input features $X$ and the output labels $Y$.

Key Characteristics:

• Learning Approach: They model how data is generated in order to be able to generate new data.
• Applications: Data generation, unsupervised learning, semi-supervised learning, and density estimation.
• Examples:
  • Generative Adversarial Networks (GANs): Composed of two neural networks, a generator and a discriminator, which are trained together to produce realistic data.
  • Variational Autoencoders (VAEs): Learn to encode data into a latent space and decode it back, allowing for data generation.
  • Naive Bayes: A simple probabilistic classifier that applies Bayes' theorem.

Discriminative AI

Discriminative AI models, on the other hand, focus on learning the decision boundary between different classes. These models directly learn the conditional probability distribution $P(Y | X)$, which means they learn to map input features $X$ to output labels $Y$.

Key Characteristics:

• Learning Approach: They focus on distinguishing between different classes of data.
• Applications: Classification, regression, and other supervised learning tasks.
• Examples:
  • Logistic Regression: A linear model for binary classification that estimates the probability that a given input belongs to a certain class.
  • Support Vector Machines (SVMs): Find the optimal hyperplane that separates different classes in the feature space.
  • Neural Networks: When used for classification tasks, they learn to predict the probability distribution over classes.