{"title":"一种学习改进shearlet和TGV离散化的背驮式算法","authors":"L. Bogensperger, A. Chambolle, T. Pock","doi":"10.23967/admos.2023.013","DOIUrl":null,"url":null,"abstract":"Summary. This work demonstrates how to use a piggyback-style algorithm to compute derivatives of loss functions that depend on solutions of convex-concave saddle-point problems. Two application scenarios are presented, where the piggyback primal-dual al-gorithm is used to learn an enhanced shearlet transform and an improved discretization of the second-order total generalized variation.","PeriodicalId":414984,"journal":{"name":"XI International Conference on Adaptive Modeling and Simulation","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Piggyback-Style Algorithm for Learning Improved Shearlets and TGV Discretizations\",\"authors\":\"L. Bogensperger, A. Chambolle, T. Pock\",\"doi\":\"10.23967/admos.2023.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary. This work demonstrates how to use a piggyback-style algorithm to compute derivatives of loss functions that depend on solutions of convex-concave saddle-point problems. Two application scenarios are presented, where the piggyback primal-dual al-gorithm is used to learn an enhanced shearlet transform and an improved discretization of the second-order total generalized variation.\",\"PeriodicalId\":414984,\"journal\":{\"name\":\"XI International Conference on Adaptive Modeling and Simulation\",\"volume\":\"5 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"XI International Conference on Adaptive Modeling and Simulation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23967/admos.2023.013\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"XI International Conference on Adaptive Modeling and Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23967/admos.2023.013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Piggyback-Style Algorithm for Learning Improved Shearlets and TGV Discretizations
Summary. This work demonstrates how to use a piggyback-style algorithm to compute derivatives of loss functions that depend on solutions of convex-concave saddle-point problems. Two application scenarios are presented, where the piggyback primal-dual al-gorithm is used to learn an enhanced shearlet transform and an improved discretization of the second-order total generalized variation.
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