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The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i.e.style feature, and the feature representing the invariant semantic content, i.e.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
S. Zhuo and T. Sim, “Defocus map estimation from a single image,” Pattern Recognition , vol. 44, no. 9, pp. 1852–1858, 2011
2011
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
T. Mallick, P. P. Das, and A. K. Majumdar, “Characterizations of noise in kinect depth images: A review,” IEEE Sensors journal , vol. 14, no. 6, pp. 1731–1740, 2014
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
F. Guo, J. Tang, and X. Xiao, “Foggy scene rendering based on transmission map estimation,” International Journal of Computer Games Technology , vol. 2014, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Computer Science , 2014
2014
Earlier work this paper cites.
C. Sun, B. Kong, L. He, and Q. Tian, “An algorithm of imaging simulation of fog with different visibility,” in 2015 IEEE International Conference on Information and Automation . IEEE, 2015, pp. 1607–1611
2015
Earlier work this paper cites.
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2536–2544
2016
Earlier work this paper cites.
M. F. Mathieu, J. J. Zhao, J. Zhao, A. Ramesh, P. Sprechmann, and Y. LeCun, “Disentangling factors of variation in deep representation using adversarial training,” in Advances in Neural Information Processing Systems , 2016, pp. 5040–5048
2016
Earlier work this paper cites.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in Advances in neural information processing systems , 2016, pp. 2172–2180
2016
Earlier work this paper cites.
X. Wang and A. Gupta, “Generative image modeling using style and structure adversarial networks,” in European conference on computer vision . Springer, 2016, pp. 318–335
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” computer vision and pattern recognition , pp. 2818–2826, 2016
2016
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1125–1134
2017
Earlier work this paper cites.
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in IEEE International Conference on Computer Vision , 2017, pp. 2242–2251
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
E. L. Denton et al. , “Unsupervised learning of disentangled representations from video,” in Advances in neural information processing systems , 2017, pp. 4414–4423
2017
Cited alongside, same era.
J. Zhu, R. Zhang, D. Pathak, T. Darrell, A. A. Efros, O. Wang, and E. Shechtman, “Toward multimodal image-to-image translation,” neural information processing systems , pp. 465–476, 2017
2017
Cited alongside, same era.
G. Liu, J. Wang, C. Zhang, S. Liao, and Y. Liu, “Realistic view synthesis of a structured traffic environment via adversarial training,” in 2017 Chinese Automation Congress (CAC) , 2017, pp. 6600–6605
2017
Cited alongside, same era.
A. Sarker, M. Akter, and M. S. Uddin, “Simulation of hazy image and validation of haze removal technique,” Journal of Computer and Communications , vol. 7, no. 2, pp. 62–72, 2019
2019
Later among the works it cites.
C. Sweeney, G. Izatt, and R. Tedrake, “A supervised approach to predicting noise in depth images,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 796–802
2019
Later among the works it cites.
L. Du, H. Hu, and Y. Wu, “Age factor removal network based on transfer learning and adversarial learning for cross-age face recognition,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 9, pp. 2830–2842, 2019
2019
Later among the works it cites.
H. Zhang, V. Sindagi, and V. M. Patel, “Image de-raining using a conditional generative adversarial network,” IEEE transactions on circuits and systems for video technology , vol. 30, no. 11, pp. 3943–3956, 2019
2019
Later among the works it cites.
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M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” neural information processing systems , pp. 6626–6637, 2017
2017
Cited alongside, same era.
W. R. Scott, Group theory . Courier Corporation, 2012
2017
Cited alongside, same era.
2018
Cited alongside, same era.
X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz, “Multimodal unsupervised image-to-image translation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 172–189
2018
Cited alongside, same era.
H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang, “Diverse image-to-image translation via disentangled representations,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 35–51
2018
Cited alongside, same era.
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro, “High-resolution image synthesis and semantic manipulation with conditional gans,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8798–8807
2018
Cited alongside, same era.
D. Engin, A. Genç, and H. Kemal Ekenel, “Cycle-dehaze: Enhanced cyclegan for single image dehazing,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 825–833
2018
Cited alongside, same era.
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8789–8797
2018
Cited alongside, same era.
Y. Qu, Y. Chen, J. Huang, and Y. Xie, “Enhanced pix2pix dehazing network,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8160–8168
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Xu, D. Liu, and Z. Xiong, “E2i: Generative inpainting from edge to image,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, no. 4, pp. 1308–1322, 2020
2020
Later among the works it cites.
S. Ge, C. Li, S. Zhao, and D. Zeng, “Occluded face recognition in the wild by identity-diversity inpainting,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 10, pp. 3387–3397, 2020
2020
Later among the works it cites.
D.-W. Jaw, S.-C. Huang, and S.-Y. Kuo, “Desnowgan: An efficient single image snow removal framework using cross-resolution lateral connection and gans,” IEEE Transactions on Circuits and Systems for Video Technology , 2020
2020
Later among the works it cites.
Z. Zhu, Y. Meng, D. Kong, X. Zhang, Y. Guo, and Y. Zhao, “To see in the dark: N2dgan for background modeling in nighttime scene,” IEEE Transactions on Circuits and Systems for Video Technology , 2020
2020
Later among the works it cites.
L. Xu, C. Zhang, Y. Liu, L. Wang, and L. Li, “Worst perception scenario search for autonomous driving,” in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2020, pp. 1702–1707
2020
Later among the works it cites.
X. Zhang, F. Zhanga, and C. Xu, “Joint expression synthesis and representation learning for facial expression recognition,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.
Y. Xia, W. Zheng, Y. Wang, H. Yu, J. Dong, and F.-Y. Wang, “Local and global perception generative adversarial network for facial expression synthesis,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.
Y. Ding, M. Li, T. Yan, F. Zhang, Y. Liu, and R. W. Lau, “Rain streak removal from light field images,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.
M. Ju, C. Ding, W. Ren, and Y. Yang, “Idbp: Image dehazing using blended priors including non-local, local, and global priors,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Closest in time.