What is a fundamental distinction between the learning processes of generative and discriminative models?

The fundamental distinction lies in how the learning processes of these models are structured. Discriminative models are specifically designed to focus on the boundaries between classes by learning the conditional probability of the target labels given the input features. This means that they analyze and differentiate instances based solely on the input data features, aiming to classify or predict outcomes without needing to understand the underlying structure of the data itself.

In contrast, generative models take a more holistic approach by attempting to learn the joint distribution of the features and the labels. They aim to understand how the data is generated, which involves modeling the entire data distribution. This foundational difference is crucial because it affects how each model behaves during training and inference, as well as their respective strengths and weaknesses in various applications.

By capturing only the input data features, discriminative models can be more efficient in certain classification tasks, while generative models have broader capabilities, such as generating new data points or filling in missing data based on learned distributions.