{"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/end-to-end-reconstruction-meets-data-driven","title":"End-to-end reconstruction meets data-driven regularization for inverse problems","arxiv_id":"2106.03538","date":"2021-06-07","proceeding":"NeurIPS 2021 12","authors":["Subhadip Mukherjee","Marcello Carioni","Ozan Öktem","Carola-Bibiane Schönlieb"],"abstract":"We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems. The proposed method combines the classical variational framework with iterative unrolling, which essentially seeks to minimize a weighted combination of the expected distortion in the measurement space and the Wasserstein-1 distance between the distributions of the reconstruction and ground-truth. More specifically, the regularizer in the variational setting is parametrized by a deep neural network and learned simultaneously with the unrolled reconstruction operator. The variational problem is then initialized with the reconstruction of the unrolled operator and solved iteratively till convergence. Notably, it takes significantly fewer iterations to converge, thanks to the excellent initialization obtained via the unrolled operator. The resulting approach combines the computational efficiency of end-to-end unrolled reconstruction with the well-posedness and noise-stability guarantees of the variational setting. Moreover, we demonstrate with the example of X-ray computed tomography (CT) that our approach outperforms state-of-the-art unsupervised methods, and that it outperforms or is on par with state-of-the-art supervised learned reconstruction approaches.","url_abs":"https://arxiv.org/abs/2106.03538v1","url_pdf":"https://arxiv.org/pdf/2106.03538v1.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":"end-to-end-reconstruction-meets-data-driven","repo_url":"https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"end-to-end-reconstruction-meets-data-driven","repo_url":"https://github.com/sulam-group/learned-proximal-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"unrolling","task_name":"Rolling Shutter Correction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.03538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03538"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sulam-group/learned-proximal-networks","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"068e5121b893e599","entry":"compute_gradient_penalty","repo":"Subhadip-1/unrolling_meets_data_driven_regularization","repo_kind":"official","path":"adversarial_reg_models.py","file_url":"https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization/blob/HEAD/adversarial_reg_models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"068e5121b893e599"}},{"code_sha256_prefix":"d932e27fd78db742","entry":"copy_if_zero_strides","repo":"Subhadip-1/unrolling_meets_data_driven_regularization","repo_kind":"official","path":"torch_wrapper.py","file_url":"https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization/blob/HEAD/torch_wrapper.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d932e27fd78db742"}},{"code_sha256_prefix":"17723f0de11cfd23","entry":"cut_image","repo":"Subhadip-1/unrolling_meets_data_driven_regularization","repo_kind":"official","path":"mayo_utils.py","file_url":"https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization/blob/HEAD/mayo_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"17723f0de11cfd23"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}