Генеративная модель — модель, изучающая распределение или структуру обучающих данных, чтобы создавать новые примеры или оценивать вероятность примеров. Она может генерировать контент, симулировать данные или поддерживать такие задачи, как обучение представлений и обнаружение аномалий.
Practically speaking, a model that does either of the following: - Creates (generates) new examples from the training dataset. For example, a generative model could create poetry after training on a dataset of poems. The generator part of a generative adversarial network falls into this category. - Determines the probability that a new example comes from the training set, or was created from the same mechanism that created the training set. For example, after training on a dataset consisting of English sentences, a generative model could determine the probability that new input is a valid English sentence. A generative model can theoretically discern the distribution of examples or particular features in a dataset. That is: Unsupervised learning models are generative. Contrast with discriminative models.
A type of machine learning model that can create novel outputs based on its training data. At its simplest, the model generates new data that looks like a certain set of categories that it was trained on. Usually associated with large language models, but other types of models can be generative as well.