Basé sur le flux Modèles Génératifs are a class of generative models in apprentissage automatique that leverage the concept of invertible transformations to create complex data distributions. Unlike traditional generative models that sample from a espace latent, modèles basés sur le flux use a series of bijective (one-to-one) transformations to map simple distributions, such as a distribution gaussienne, into more complex data distributions.
Ces modèles se caractérisent par leur capacité à effectuer une estimation exacte de la vraisemblance, which allows them to generate new samples and evaluate the probability of observed data efficiently. The architecture typically consists of stacking multiple layers of transformations, such as affine transformations and nonlinear activation functions, to build a deep network that can capture intricate data structures.
One of the key advantages of flow-based models is their flexibility. They can model high-dimensional data, including images and audio, making them suitable for various applications in generative tasks. By training on a dataset, these models learn the underlying distribution des données and can then generate new, similar data points by passing samples from the simple distribution through the learned transformations.
Flow-Based Generative Models have gained popularity due to their robustness and the interpretability of their transformations. They can also be combined with other generative approaches, such as variational autoencoders and generative adversarial networks, to enhance their performance and capabilities.