How Generative Models Learn Data Distributions Instead of Memorizing Datasets

Generative models aim to approximate the underlying probability structure of data rather than memorize individual examples. Since the true data distribution is unknown, a parameterized model distribution is trained on observed samples to serve as a learned approximation. This approach allows the model to both evaluate how plausible a data point is and generate entirely new samples from the learned structure. High-dimensional data makes this task harder, requiring models that balance flexibility with computational tractability. The same learned distribution supports multiple applications, including data generation, density estimation, and unsupervised representation learning.