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2020

DCTRGAN: Improving the Precision of Generative Models with Reweighting

Diefenbacher, Sascha, Eren, Engin, Kasieczka, Gregor et al.

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Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (GANs).

We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.
The correction takes the form of a reweighting function that can be applied to generated examples when making predictions from the simulation.
We illustrate this approach using GANs trained on standard multimodal probability densities as well as calorimeter simulations from high energy physics.

DCTRGAN: Improving the Precision of Generative Models with Reweighting · Around

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