DCTRGAN: Improving the Precision of Generative Models with Reweighting
- URL: http://arxiv.org/abs/2009.03796v1
- Date: Thu, 3 Sep 2020 18:00:27 GMT
- Title: DCTRGAN: Improving the Precision of Generative Models with Reweighting
- Authors: Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol,
Benjamin Nachman, and David Shih
- Abstract summary: We introduce a post-hoc correction to deep generative models to further improve their fidelity.
The correction takes the form of a reweighting function that can be applied to generated examples.
We show that the weighted GAN examples significantly improve the accuracy of the generated samples without a large loss in statistical power.
- Score: 1.2622634782102324
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. We show that the weighted GAN
examples significantly improve the accuracy of the generated samples without a
large loss in statistical power. This approach could be applied to any
generative model and is a promising refinement method for high energy physics
applications and beyond.
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