{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/solving-linear-inverse-problems-using-gan","title":"Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees","arxiv_id":"1802.08406","date":"2018-02-23","proceeding":null,"authors":["Viraj Shah","Chinmay Hegde"],"abstract":"In recent works, both sparsity-based methods as well as learning-based\nmethods have proven to be successful in solving several challenging linear\ninverse problems. However, sparsity priors for natural signals and images\nsuffer from poor discriminative capability, while learning-based methods seldom\nprovide concrete theoretical guarantees. In this work, we advocate the idea of\nreplacing hand-crafted priors, such as sparsity, with a Generative Adversarial\nNetwork (GAN) to solve linear inverse problems such as compressive sensing. In\nparticular, we propose a projected gradient descent (PGD) algorithm for\neffective use of GAN priors for linear inverse problems, and also provide\ntheoretical guarantees on the rate of convergence of this algorithm. Moreover,\nwe show empirically that our algorithm demonstrates superior performance over\nan existing method of leveraging GANs for compressive sensing.","url_abs":"http://arxiv.org/abs/1802.08406v1","url_pdf":"http://arxiv.org/pdf/1802.08406v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"solving-linear-inverse-problems-using-gan","repo_url":"https://github.com/shahviraj/pgdgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"compressive-sensing","task_name":"Compressive Sensing"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.08406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.08406"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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