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Image inpainting techniques have shown promising improvement with the assistance of generative adversarial networks (GANs) recently.
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2017
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J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, “Generative image inpainting with contextual attention,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 5505–5514
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C. Zheng, T.-J. Cham, and J. Cai, “Pluralistic image completion,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 1438–1447
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Y. Zeng, J. Fu, H. Chao, and B. Guo, “Learning pyramid-context encoder network for high-quality image inpainting,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 1486–1494
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T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 4401–4410
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A. Jolicoeur-Martineau, “The relativistic discriminator: a key element missing from standard gan,” in International Conference for Learning Representations (ICLR) , 2019
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Y. Wang, X. Tao, X. Qi, X. Shen, and J. Jia, “Image inpainting via generative multi-column convolutional neural networks,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018, pp. 331–340
2018
Cited alongside, same era.
Y. Song, C. Yang, Z. Lin, X. Liu, Q. Huang, H. Li, and C.-C. Jay Kuo, “Contextual-based image inpainting: Infer, match, and translate,” in European Conference on Computer Vision (ECCV) , 2018, pp. 3–19
2018
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Z. Yan, X. Li, M. Li, W. Zuo, and S. Shan, “Shift-net: Image inpainting via deep feature rearrangement,” in European Conference on Computer Vision (ECCV) , 2018, pp. 1–17
2018
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G. Liu, F. A. Reda, K. J. Shih, T.-C. Wang, A. Tao, and B. Catanzaro, “Image inpainting for irregular holes using partial convolutions,” in European Conference on Computer Vision (ECCV) , 2018, pp. 85–100
2018
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X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C. C. Loy, “Esrgan: Enhanced super-resolution generative adversarial networks,” in European Conference on Computer Vision Workshop (ECCVW) , 2018, pp. 63–79
2018
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2018
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2018
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T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in International Conference for Learning Representations (ICLR) , 2018
2018
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J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. Huang, “Free-form image inpainting with gated convolution,” in IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 4471–4480
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2019
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J. Li, F. He, L. Zhang, B. Du, and D. Tao, “Progressive reconstruction of visual structure for image inpainting,” in IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 5962–5971
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2019
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Y. Ren, X. Yu, R. Zhang, T. H. Li, S. Liu, and G. Li, “Structureflow: Image inpainting via structure-aware appearance flow,” in IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 181–190
2019
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M. cheol Sagong, Y. goo Shin, S. wook Kim, S. Park, and S. jea Ko, “Pepsi: Fast image inpainting with parallel decoding network,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 11 360–11 368
2019
Later among the works it cites.
C. Xie, S. Liu, C. Li, M.-M. Cheng, W. Zuo, X. Liu, S. Wen, and E. Ding, “Image inpainting with learnable bidirectional attention maps,” in IEEE International Conference on Computer Vision (ICCV) , 2019, pp. 8858–8867
2019
Later among the works it cites.
Z. Hui, J. Li, X. Gao, and X. Wang, “Progressive perception-oriented network for single image super-resolution,” Information Sciences , vol. 546, pp. 769–786, 2021
2021
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