BibTex

File contents

@article{Andreou_2023,
doi = {10.1088/1748-0221/18/06/P06007},
url = {https://doi.org/10.1088/1748-0221/18/06/P06007},
year = {2023},
month = {jun},
publisher = {IOP Publishing},
volume = {18},
number = {06},
pages = {P06007},
author = {Andreou, I. and Mouelle, N.},
title = {Evaluating Generative Adversarial Networks for particle hit generation in a cylindrical drift chamber using Fréchet Inception Distance},
journal = {Journal of Instrumentation},
abstract = {We use Fréchet Inception Distance (FID) measured in the latent spaces of pre-trained, fine-tuned and custom-made inception networks to evaluate Generative Adversarial Networks (GANs) developed by the COherent Muon to Electron Transition (COMET) collaboration to generate sequences of background hits in a Cylindrical Drift Chamber (CDC). We validate the convergence of the GANs' training and show that the use of self-attention layers reduces FID. Our method enables the use of FID as an evaluation metric even when an application-specific inception network is not readily available, making it transferable to other GAN applications in High Energy Physics.}
}