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We devise a novel paradigm for learning a representation that captures an orders-of-magnitude variety of scales from an unstructured collection of ordinary images. We treat this collection as a distribution of scale-space slices to be learned using adversarial training, and additionally enforce coherency across slices. Our approach relies on a multiscale generator with carefully injected procedural frequency content, which allows to interactively explore the emerging continuous scale space. Training across vastly different scales poses challenges regarding stability, which we tackle using a supervision scheme that involves careful sampling of scales. We show that our generator can be used as a multiscale generative model, and for reconstructions of scale spaces from unstructured patches. Significantly outperforming the state of the art, we demonstrate zoom-in factors of up to 256x at high quality and scale consistency.<\/jats:p>","DOI":"10.1145\/3658190","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T14:47:57Z","timestamp":1721400477000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning Images Across Scales Using Adversarial Training"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2290-0299","authenticated-orcid":false,"given":"Krzysztof","family":"Wolski","sequence":"first","affiliation":[{"name":"Max-Planck-Institut f\u00fcr Informatik, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1919-450X","authenticated-orcid":false,"given":"Adarsh","family":"Djeacoumar","sequence":"additional","affiliation":[{"name":"Max-Planck-Institut f\u00fcr Informatik, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4926-1566","authenticated-orcid":false,"given":"Alireza","family":"Javanmardi","sequence":"additional","affiliation":[{"name":"Max-Planck-Institut f\u00fcr Informatik, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1343-8613","authenticated-orcid":false,"given":"Hans-Peter","family":"Seidel","sequence":"additional","affiliation":[{"name":"Max-Planck-Institut f\u00fcr Informatik, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-6625","authenticated-orcid":false,"given":"Christian","family":"Theobalt","sequence":"additional","affiliation":[{"name":"Max-Planck-Institut f\u00fcr Informatik, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0124-0180","authenticated-orcid":false,"given":"Guillaume","family":"Cordonnier","sequence":"additional","affiliation":[{"name":"Inria, Universit\u00e9 C\u00f4te d'Azur, Sophia-Antipolis, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8505-4141","authenticated-orcid":false,"given":"Karol","family":"Myszkowski","sequence":"additional","affiliation":[{"name":"Max-Planck-Institut f\u00fcr Informatik, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9254-4819","authenticated-orcid":false,"given":"George","family":"Drettakis","sequence":"additional","affiliation":[{"name":"Inria, Universit\u00e9 C\u00f4te d'Azur, Sophia-Antipolis, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5825-9467","authenticated-orcid":false,"given":"Xingang","family":"Pan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7784-7957","authenticated-orcid":false,"given":"Thomas","family":"Leimk\u00fchler","sequence":"additional","affiliation":[{"name":"Max-Planck-Institut f\u00fcr Informatik, Saarbr\u00fccken, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Digital signal processing","author":"Antoniou Andreas","unstructured":"Andreas Antoniou. 2006. 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BACON: Band-Limited Coordinate Networks for Multiscale Scene Representation. In CVPR. 16252--16262."},{"key":"e_1_2_2_54_1","unstructured":"Liying Lu Wenbo Li Xin Tao Jiangbo Lu and Jiaya Jia. 2021. MASA-SR: Matching Acceleration and Spatial Adaptation for Reference-Based Image Super-Resolution. In CVPR. 6368--6377."},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.192463"},{"key":"e_1_2_2_56_1","volume-title":"The fractal geometry of nature","author":"Mandelbrot Benoit B.","unstructured":"Benoit B. Mandelbrot. 1982. The fractal geometry of nature. Vol. 1. WH freeman New York."},{"key":"e_1_2_2_57_1","series-title":"Series B. Biological Sciences 207, 1167","volume-title":"Theory of edge detection. Proceedings of the Royal Society of London","author":"Marr David","year":"1980","unstructured":"David Marr and Ellen Hildreth. 1980. Theory of edge detection. Proceedings of the Royal Society of London. Series B. 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