It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems

Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin. It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. pages 18152-18164, 2021. [doi]

@inproceedings{CohenBFR21,
  title = {It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems},
  author = {Regev Cohen and Yochai Blau and Daniel Freedman and Ehud Rivlin},
  year = {2021},
  url = {https://proceedings.neurips.cc/paper/2021/hash/97108695bd93b6be52fa0334874c8722-Abstract.html},
  researchr = {https://researchr.org/publication/CohenBFR21},
  cites = {0},
  citedby = {0},
  pages = {18152-18164},
  booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual},
  editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan},
}