{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:35:09Z","timestamp":1761060909476,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T00:00:00Z","timestamp":1571097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Shanghai Jiao Tong University","award":["WF220403029","WF220103010"],"award-info":[{"award-number":["WF220403029","WF220103010"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,10,15]]},"DOI":"10.1145\/3343031.3350952","type":"proceedings-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T16:32:26Z","timestamp":1571675546000},"page":"756-764","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["GAN Flexible Lmser for Super-resolution"],"prefix":"10.1145","author":[{"given":"Peiying","family":"Li","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shikui","family":"Tu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,10,15]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"e_1_3_2_1_2_1","unstructured":"Dana H. Ballard. 1987. Modular Learning in Neural Networks. In AAAI. 279--284.  Dana H. Ballard. 1987. Modular Learning in Neural Networks. In AAAI. 279--284."},{"key":"e_1_3_2_1_3_1","volume-title":"Auto-association by multilayer perceptrons and singular value decomposition. Biological cybernetics","author":"Bourlard Herv\u00e9","year":"1988","unstructured":"Herv\u00e9 Bourlard and Yves Kamp . 1988. Auto-association by multilayer perceptrons and singular value decomposition. Biological cybernetics , Vol. 59 , 4--5 ( 1988 ), 291--294. Herv\u00e9 Bourlard and Yves Kamp. 1988. Auto-association by multilayer perceptrons and singular value decomposition. Biological cybernetics , Vol. 59, 4--5 (1988), 291--294."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.116"},{"key":"e_1_3_2_1_5_1","volume-title":"Use a GAN to Learn How to Do Image Degradation First. In Computer Vision -- ECCV","author":"Bulat Adrian","year":"2018","unstructured":"Adrian Bulat , Jing Yang , and Georgios Tzimiropoulos . 2018. To Learn Image Super-Resolution , Use a GAN to Learn How to Do Image Degradation First. In Computer Vision -- ECCV 2018 , Vittorio Ferrari, Martial Hebert , Cristian Sminchisescu, and Yair Weiss (Eds.). Springer International Publishing , Cham, 187--202. Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos. 2018. To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image Degradation First. In Computer Vision -- ECCV 2018, Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (Eds.). Springer International Publishing, Cham, 187--202."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/FG.2018.00020"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"e_1_3_2_1_10_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680.  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680."},{"key":"e_1_3_2_1_11_1","volume-title":"Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385","author":"He Kaiming","year":"2015","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2015. Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385 ( 2015 ). Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385 (2015)."},{"key":"e_1_3_2_1_12_1","unstructured":"Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems. 6626--6637.  Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In Advances in Neural Information Processing Systems. 6626--6637."},{"key":"e_1_3_2_1_13_1","volume-title":"Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution. 2017 IEEE International Conference on Computer Vision (ICCV)","author":"Huang Huaibo","year":"2017","unstructured":"Huaibo Huang , Ran He , Zhenan Sun , and Tieniu Tan . 2017 . Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution. 2017 IEEE International Conference on Computer Vision (ICCV) (2017), 1698--1706. Huaibo Huang, Ran He, Zhenan Sun, and Tieniu Tan. 2017. Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution. 2017 IEEE International Conference on Computer Vision (ICCV) (2017), 1698--1706."},{"key":"e_1_3_2_1_14_1","volume-title":"Revisit Lmser and its further development based on convolutional layers. CoRR","author":"Huang Wenjing","year":"2019","unstructured":"Wenjing Huang , Shikui Tu , and Lei Xu. 2019. Revisit Lmser and its further development based on convolutional layers. CoRR , Vol. abs\/ 1904 .06307 ( 2019 ). arxiv: 1904.06307 Wenjing Huang, Shikui Tu, and Lei Xu. 2019. Revisit Lmser and its further development based on convolutional layers. CoRR , Vol. abs\/1904.06307 (2019). arxiv: 1904.06307"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2011.6130513"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.618"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"e_1_3_2_1_20_1","volume-title":"Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections. CoRR","author":"Mao Xiao-Jiao","year":"2016","unstructured":"Xiao-Jiao Mao , Chunhua Shen , and Yu-Bin Yang . 2016. Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections. CoRR , Vol. abs\/ 1606 .08921 ( 2016 ). Xiao-Jiao Mao, Chunhua Shen, and Yu-Bin Yang. 2016. Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections. CoRR , Vol. abs\/1606.08921 (2016)."},{"key":"e_1_3_2_1_21_1","volume-title":"Gated Fusion Network for Single Image Dehazing. CoRR","author":"Ren Wenqi","year":"2018","unstructured":"Wenqi Ren , Lin Ma , Jiawei Zhang , Jinshan Pan , Xiaochun Cao , Wei Liu , and Ming-Hsuan Yang . 2018. Gated Fusion Network for Single Image Dehazing. CoRR , Vol. abs\/ 1804 .00213 ( 2018 ). arxiv: 1804.00213 http:\/\/arxiv.org\/abs\/1804.00213 Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. 2018. Gated Fusion Network for Single Image Dehazing. CoRR , Vol. abs\/1804.00213 (2018). arxiv: 1804.00213 http:\/\/arxiv.org\/abs\/1804.00213"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.298"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.486"},{"key":"e_1_3_2_1_25_1","volume-title":"NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","author":"Timofte Radu","year":"2017","unstructured":"Radu Timofte , Eirikur Agustsson , Luc Van Gool , Ming-Hsuan Yang , Lei Zhang , Bee Lim , Sanghyun Son , Heewon Kim , and et al. Seungjun Nah. 2017 . NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ( 2017 ), 1110--1121. Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, and et al. Seungjun Nah. 2017. NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017), 1110--1121."},{"key":"e_1_3_2_1_26_1","volume-title":"Deep Networks for Image Super-Resolution with Sparse Prior. In 2015 IEEE International Conference on Computer Vision (ICCV). 370--378","author":"Wang Zhaowen","year":"2015","unstructured":"Zhaowen Wang , Ding Liu , Jianchao Yang , Wei Han , and Thomas Huang . 2015 . Deep Networks for Image Super-Resolution with Sparse Prior. In 2015 IEEE International Conference on Computer Vision (ICCV). 370--378 . https:\/\/doi.org\/10.1109\/ICCV.2015.50 10.1109\/ICCV.2015.50 Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang. 2015. Deep Networks for Image Super-Resolution with Sparse Prior. In 2015 IEEE International Conference on Computer Vision (ICCV). 370--378. https:\/\/doi.org\/10.1109\/ICCV.2015.50"},{"key":"e_1_3_2_1_27_1","volume-title":"Proc. of 1991 International Joint Conference on Neural Networks. 2363--2373","author":"Xu Lei","year":"1991","unstructured":"Lei Xu . 1991 . Least MSE Reconstruction for Self-Organization: (I)&(II) . In Proc. of 1991 International Joint Conference on Neural Networks. 2363--2373 . Lei Xu. 1991. Least MSE Reconstruction for Self-Organization: (I)&(II). In Proc. of 1991 International Joint Conference on Neural Networks. 2363--2373."},{"key":"e_1_3_2_1_28_1","volume-title":"Least mean square error reconstruction principle for self-organizing neural-nets. Neural networks","author":"Lei Xu.","year":"1993","unstructured":"Lei Xu. 1993. Least mean square error reconstruction principle for self-organizing neural-nets. Neural networks , Vol. 6 , 5 ( 1993 ), 627--648. Lei Xu. 1993. Least mean square error reconstruction principle for self-organizing neural-nets. Neural networks , Vol. 6, 5 (1993), 627--648."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2019.1911603"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.31193\/ssap.01.9787509791011"},{"key":"e_1_3_2_1_31_1","volume-title":"Learning face representation from scratch. arXiv preprint arXiv:1411.7923","author":"Yi Dong","year":"2014","unstructured":"Dong Yi , Zhen Lei , Shengcai Liao , and Stan Z Li. 2014. Learning face representation from scratch. arXiv preprint arXiv:1411.7923 ( 2014 ). Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. 2014. Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_20"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.570"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_37"}],"event":{"name":"MM '19: The 27th ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Nice France","acronym":"MM '19"},"container-title":["Proceedings of the 27th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3343031.3350952","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3343031.3350952","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:13:17Z","timestamp":1750201997000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3343031.3350952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,15]]},"references-count":35,"alternative-id":["10.1145\/3343031.3350952","10.1145\/3343031"],"URL":"https:\/\/doi.org\/10.1145\/3343031.3350952","relation":{},"subject":[],"published":{"date-parts":[[2019,10,15]]},"assertion":[{"value":"2019-10-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}