Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training

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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