Additionally, it's worth noting that generative and discriminative models can be complementary and used together in certain applications. For example, generative models can be used for data augmentation, which can then be used to train a discriminative model with more data. Another example is the use of generative models for unsupervised learning to learn the underlying structure of the data, which can then be used to improve the performance of a discriminative model. Ultimately, the choice between generative and discriminative models should be based on the specific problem and the available data.
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