In artificial intelligence, a generative model is one that can generate new data or results based on given inputs. It generates fresh data that is consistent with the training data and the underlying probability distributions of the model. A discriminative model, on the other hand, is one that has been trained on labelled data and can categorise incoming data into one or more predetermined categories. Generative models are better at creating new data, but discriminative models are better at classifying data. The trade-off between the two approaches is that generative models can demand significantly more data and computational capacity, whereas discriminative models are faster and require less data.
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