[29] Andreas Lugmayr, Martin Danelljan, Radu Timofte, Manuel Fritsche, Shuhang Gu, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, AN Rajagoapalan, Nam Hyung Joon, et al. Aim 2019 challenge on real-world image super-resolution: Methods and results. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pages 3575-3583. IEEE, 2019.
[30] Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3994-4003, 2016.
[31] Chong Mou, Yanze Wu, Xintao Wang, Chao Dong, Jian Zhang, and Ying Shan. Metric learning based interactive modulation for real-world super-resolution. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII, pages 723-740. Springer, 2022.
[32] Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098, 2017.
[33] Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. Latent multi-task architecture learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4822-4829, 2019.
[34] Ozan Sener and Vladlen Koltun. Multi-task learning as multi-objective optimization. Advances in neural information processing systems, 31, 2018.
[35] Jiayi Shen, Xiantong Zhen, Marcel Worring, and Ling Shao. Variational multi-task learning with gumbel-softmax priors. Advances in Neural Information Processing Systems, 34:21031-21042, 2021.
[36] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. ICLR, 2015.
[37] Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. Which tasks should be learned together in multi-task learning? In International Conference on Machine Learning, pages 9120-9132. PMLR, 2020.
[38] Longguang Wang, Yingqian Wang, Xiaoyu Dong, Qingyu Xu, Jungang Yang, Wei An, and Yulan Guo. Unsupervised degradation representation learning for blind super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10581-10590, 2021.
[39] Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan. Real-esrgan: Training real-world blind super-resolution with pure synthetic data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1905-1914, 2021.
[40] Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. arXiv preprint arXiv:1804.02815, 2018.
[41] Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, and Xiaou Tang. Esrgan: Enhanced super-resolution generative adversarial networks. 2018.
[42] Zhang Wenlong, Liu Yihao, Chao Dong, and Yu Qiao. Ranksrgan: Generative adversarial networks with ranker for image super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[43] Yanze Wu, Xintao Wang, Gen Li, and Ying Shan. Animesr: Learning real-world super-resolution models for animation videos. In Advances in Neural Information Processing Systems.
[44] Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, and Ying Shan. Finding discriminative filters for specific degradations in blind super-resolution. Advances in Neural Information Processing Systems, 34:51-61, 2021.