That's a great point! In some cases, using a combination of both generative and discriminative models can lead to better results. For example, in image classification, a generative model like a GAN can be used to generate new training images that can be used to augment the existing dataset. This can help improve the accuracy of a discriminative model like a CNN, which is then trained on the augmented dataset. Additionally, a generative model like a variational autoencoder (VAE) can be used for unsupervised learning to learn a low-dimensional representation of the input data, which can then be used as input to a discriminative model. The choice between using generative, discriminative, or a combination of both models depends on the specific problem and the available data.
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