フローベース 生成モデル are a class of generative models in 機械学習 that leverage the concept of invertible transformations to create complex data distributions. Unlike traditional generative models that sample from a 潜在空間, フローベースモデル use a series of bijective (one-to-one) transformations to map simple distributions, such as a ガウス分布, into more complex data distributions.
これらのモデルは正確な 尤度推定を行う能力によって特徴付けられます, 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 データ分布 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.