Generative models are a class of statistical models that are used to generate new data points that are similar to a given dataset. They learn the underlying distribution of the data and can synthesize new samples from that distribution. Generative models can be applied to various types of data, including images, text, and audio.
The key feature of generative models is their ability to create new instances that are not just copies of the training data but instead are novel combinations that reflect the learned patterns. Some popular types of generative models include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and normalizing flows.
Generative models are widely used in applications such as image generation, text completion, style transfer, and more, making them a vital area of research in machine learning and artificial intelligence. The effectiveness of a generative model is typically evaluated based on how realistic or coherent the generated data appears compared to the original training data.
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