👉 Generators are a class of models in machine learning, particularly within the realm of generative models, designed to produce new data instances that resemble a given dataset. They work by learning the underlying probability distribution of the input data, typically through an encoder-decoder architecture. The encoder maps input data into a latent space representation, capturing the essential features and patterns of the data. The decoder then takes this latent representation and generates new data points by sampling from this distribution, effectively creating novel outputs that are similar to the original dataset. This process is often used in tasks like image generation, text synthesis, and data augmentation, where the goal is to produce realistic and diverse new instances based on learned patterns.
