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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 algorithm is used to learn an enhanced shearlet transform and an improved discretization of the second-order total generalized variation. | 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 algorithm is used to learn an enhanced shearlet transform and an improved discretization of the second-order total generalized variation. | ||
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| + | == Full Paper == | ||
| + | <pdf>Media:Draft_Sanchez Pinedo_76010630673_file.pdf</pdf> | ||
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| + | ==Video== | ||
| + | {{#evt:service=cloudfront|id=332774|alignment=center|filename=Piggyback-2.mp4}} | ||
Latest revision as of 09:11, 12 July 2023
Abstract
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 algorithm is used to learn an enhanced shearlet transform and an improved discretization of the second-order total generalized variation.
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Published on 26/05/23
Submitted on 26/05/23
Volume Adaptive Modelling, Optimisation and Learning Strategies for Image Analysis, 2023
DOI: 10.23967/admos.2023.013
Licence: CC BY-NC-SA license
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