Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training. Cai, Y., Hu, X., Wang, H., Zhang, Y., Pfister, H., & Wei, D. In Ranzato, M., Beygelzimer, A., Dauphin, Y. N., Liang, P., & Vaughan, J. W., editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 3259–3270, 2021. Paper bibtex @inproceedings{DBLP:conf/nips/CaiHWZPW21,
author = {Yuanhao Cai and
Xiaowan Hu and
Haoqian Wang and
Yulun Zhang and
Hanspeter Pfister and
Donglai Wei},
editor = {Marc'Aurelio Ranzato and
Alina Beygelzimer and
Yann N. Dauphin and
Percy Liang and
Jennifer Wortman Vaughan},
title = {Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware
Adversarial Training},
booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference
on Neural Information Processing Systems 2021, NeurIPS 2021, December
6-14, 2021, virtual},
pages = {3259--3270},
year = {2021},
url = {https://proceedings.neurips.cc/paper/2021/hash/1a5b1e4daae265b790965a275b53ae50-Abstract.html},
timestamp = {Mon, 27 Jun 2022 01:00:00 +0200},
biburl = {https://dblp.org/rec/conf/nips/CaiHWZPW21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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