What do you understand by generative models?

Generative models are a class of machine learning algorithms that learn to model the joint probability distribution of input data, enabling them to generate new, synthetic samples that resemble the training data. Unlike discriminative models, which predict labels given inputs, generative models capture the underlying data distribution. Common families include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models such as PixelCNN or GPT. GANs consist of a generator that creates fake samples and a discriminator that tries to distinguish real from fake, training them in a minimax game. VAEs encode data into a latent space and then decode it, optimizing a reconstruction loss plus a KL divergence term. Autoregressive models predict each token conditioned on previous tokens, making them effective for text generation. Applications span image synthesis, style transfer, data augmentation, and text generation. Understanding the trade‑offs—e.g., mode collapse in GANs, blurry outputs in VAEs, and computational cost in autoregressive models—is essential for selecting the right approach for a given problem.