CycleGAN

CycleGAN and pix2pix in PyTorch. We provide PyTorch implementations for both unpaired and paired image-to-image translation. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang. This PyTorch implementation produces results comparable to or better than our original Torch software. If you would like to reproduce the same results as in the papers, check out the original CycleGAN Torch and pix2pix Torch code.


References in zbMATH (referenced in 65 articles )

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  1. Baskerville, Nicholas P.; Keating, Jonathan P.; Mezzadri, Francesco; Najnudel, Joseph: A spin glass model for the loss surfaces of generative adversarial networks (2022)
  2. Gong, Li-Hua; Xiang, Ling-Zhi; Liu, Si-Hang; Zhou, Nan-Run: Born machine model based on matrix product state quantum circuit (2022)
  3. Hassanaly, Malik; Glaws, Andrew; Stengel, Karen; King, Ryan N.: Adversarial sampling of unknown and high-dimensional conditional distributions (2022)
  4. Jain, Niharika; Olmo, Alberto; Sengupta, Sailik; Manikonda, Lydia; Kambhampati, Subbarao: Imperfect imaGANation: implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses (2022)
  5. Li, Hong-an; Zhang, Min; Yu, Zhenhua; Li, Zhanli; Li, Na: An improved pix2pix model based on Gabor filter for robust color image rendering (2022)
  6. Oh, Sehyeok; Lee, Seungcheol; Son, Myeonggyun; Kim, Jooha; Ki, Hyungson: Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning (2022)
  7. Yang, Hongkang; E, Weinan: Generalization error of GAN from the discriminator’s perspective (2022)
  8. Amerini, Irene; Anagnostopoulos, Aris; Maiano, Luca; Celsi, Lorenzo Ricciardi: Deep learning for multimedia forensics (2021)
  9. Benny, Yaniv; Galanti, Tomer; Benaim, Sagie; Wolf, Lior: Evaluation metrics for conditional image generation (2021)
  10. Canchumuni, Smith W. A.; Castro, Jose D. B.; Potratz, Júlia; Emerick, Alexandre A.; Pacheco, Marco Aurélio C.: Recent developments combining ensemble smoother and deep generative networks for facies history matching (2021)
  11. Chung, Eric; Leung, Wing Tat; Pun, Sai-Mang; Zhang, Zecheng: A multi-stage deep learning based algorithm for multiscale model reduction (2021)
  12. Galanti, Tomer; Benaim, Sagie; Wolf, Lior: Risk bounds for unsupervised cross-domain mapping with IPMs (2021)
  13. Ignatiev, V. Yu.; Matveev, I. A.; Murynin, A. B.; Usmanova, A. A.; Tsurkov, V. I.: Increasing the spatial resolution of panchromatic satellite images based on generative neural networks (2021)
  14. Kim, Hyojin; Kim, Junhyuk; Won, Sungjin; Lee, Changhoon: Unsupervised deep learning for super-resolution reconstruction of turbulence (2021)
  15. Kirchmeyer, Matthieu; Gallinari, Patrick; Rakotomamonjy, Alain; Mantrach, Amin: Unsupervised domain adaptation with non-stochastic missing data (2021)
  16. Li, Haoliang; Wan, Renjie; Wang, Shiqi; Kot, Alex C.: Unsupervised domain adaptation in the wild via disentangling representation learning (2021)
  17. Li, Qi; Wang, Xingyuan; Wang, Xiaoyu; Ma, Bin; Wang, Chunpeng; Shi, Yunqing: An encrypted coverless information hiding method based on generative models (2021)
  18. Ma, Jiayi; Jiang, Xingyu; Fan, Aoxiang; Jiang, Junjun; Yan, Junchi: Image matching from handcrafted to deep features: a survey (2021)
  19. Padmanabha, Govinda Anantha; Zabaras, Nicholas: Solving inverse problems using conditional invertible neural networks (2021)
  20. Pinetz, Thomas; Kobler, Erich; Pock, Thomas; Effland, Alexander: Shared prior learning of energy-based models for image reconstruction (2021)

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