{"type":"ScholarlyArticle","title":"Enhance Image as You Like with Unpaired Learning","authors":[{"name":"Xiaopeng Sun","slug":"xiaopeng-sun","given":"Xiaopeng","family":"Sun","affiliation":""},{"name":"Muxingzi Li","slug":"muxingzi-li","given":"Muxingzi","family":"Li","affiliation":""},{"name":"Tianyu He","slug":"tianyu-he","given":"Tianyu","family":"He","affiliation":""},{"name":"Lubin Fan","slug":"lubin-fan","given":"Lubin","family":"Fan","affiliation":""}],"abstract":"Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Therefore our inference model easily adapts to various user preferences, provided with a few favorable photos from each user. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches.","doi":"10.24963/ijcai.2021/140","url":"https://ijcai.org/proceedings/2021/140","pdf":"https://ijcai.org/proceedings/2021/0140.pdf","markdown":"https://ijcai.org/proceedings/2021/140.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/140","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Computer Vision II","track":"Main Track","number":140,"pages":{"first":1011,"last":1017},"keywords":["Machine Learning Applications: Applications of Unsupervised Learning","Computer Vision: 2D and 3D Computer Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}