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Significant advances in deep learning have led to more widely used andprecise neural network-based generative models such as Generative AdversarialNetworks (GANs). We introduce a post-hoc correction to deep generative modelsto further improve their fidelity, based on the Deep neural networks using theClassification for Tuning and Reweighting (DCTR) protocol. The correction takesthe form of a reweighting function that can be applied to generated exampleswhen making predictions from the simulation. We illustrate this approach usingGANs trained on standard multimodal probability densities as well ascalorimeter simulations from high energy physics. We show that the weighted GANexamples significantly improve the accuracy of the generated samples without alarge loss in statistical power. This approach could be applied to anygenerative model and is a promising refinement method for high energy physicsapplications and beyond.

DCTRGAN: Improving the Precision of Generative Models with Reweighting