Image generation is the task of creating new images via algorithms, typically to imitate the distribution of real images. This can be done through generative models like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). For example, a GAN can be trained on a dataset of faces and then generate entirely new, realistic-looking faces that never existed. There are also explicit models like pixel RNNs or normalizing flows. Image generation might aim to produce specific kinds of images (conditional generation) – e.g., generating images from text descriptions (text-to-image, like DALL-E) or from semantic layouts. Evaluating generative models can be tricky; metrics like Inception Score or Frechet Inception Distance (FID) are used to gauge realism and diversity of generated images. Applications include content creation, data augmentation, or filling in missing data. It’s a vivid demonstration of a model understanding complex data distribution.