{"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/benchmarking-deep-inverse-models-over-time","title":"Benchmarking deep inverse models over time, and the neural-adjoint method","arxiv_id":"2009.12919","date":"2020-09-27","proceeding":"NeurIPS 2020 12","authors":["Simiao Ren","Willie Padilla","Jordan Malof"],"abstract":"We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements. Recently many new approaches based upon deep learning have arisen generating impressive results. We conceptualize these models as different schemes for efficiently, but randomly, exploring the space of possible inverse solutions. As a result, the accuracy of each approach should be evaluated as a function of time rather than a single estimated solution, as is often done now. Using this metric, we compare several state-of-the-art inverse modeling approaches on four benchmark tasks: two existing tasks, one simple task for visualization and one new task from metamaterial design. Finally, inspired by our conception of the inverse problem, we explore a solution that uses a deep learning model to approximate the forward model, and then uses backpropagation to search for good inverse solutions. This approach, termed the neural-adjoint, achieves the best performance in many scenarios.","url_abs":"https://arxiv.org/abs/2009.12919v4","url_pdf":"https://arxiv.org/pdf/2009.12919v4.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":"benchmarking-deep-inverse-models-over-time","repo_url":"https://github.com/BensonRen/BDIMNNA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[{"method_slug":"neural-adjoint","method_name":"Neural adjoint"}],"datasets_introduced":[],"methods_introduced":[{"slug":"neural-adjoint","name":"Neural adjoint","full_name":"Neural adjoint method"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2009.12919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.12919"}},"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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