Publikation: Tarsier : Evolving Noise Injection in Super-Resolution GANs
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Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by NESRGAN+, which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve NESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms NESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection.
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ROZIERE, Baptiste, Nathanael CARRAZ RAKOTONIRINA, Vlad HOSU, Andry RASOANAIVO, Hanhe LIN, Camille COUPRIE, Olivier TEYTAUD, 2021. Tarsier : Evolving Noise Injection in Super-Resolution GANs. ICPR 2020 : 25th International Conference on Pattern Recognition. Milan, Italy, 10. Jan. 2021 - 15. Jan. 2021. In: Proceedings of ICPR 2020 : 25th International Conference on Pattern Recognition. Piscataway, NJ: IEEE, 2021, pp. 7028-7035. ISBN 978-1-72818-808-9. Available under: doi: 10.1109/ICPR48806.2021.9413318BibTex
@inproceedings{Roziere2021Tarsi-55115, year={2021}, doi={10.1109/ICPR48806.2021.9413318}, title={Tarsier : Evolving Noise Injection in Super-Resolution GANs}, isbn={978-1-72818-808-9}, publisher={IEEE}, address={Piscataway, NJ}, booktitle={Proceedings of ICPR 2020 : 25th International Conference on Pattern Recognition}, pages={7028--7035}, author={Roziere, Baptiste and Carraz Rakotonirina, Nathanael and Hosu, Vlad and Rasoanaivo, Andry and Lin, Hanhe and Couprie, Camille and Teytaud, Olivier} }
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