{"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/deep-structured-prediction-with-nonlinear","title":"Deep Structured Prediction with Nonlinear Output Transformations","arxiv_id":"1811.00539","date":"2018-11-01","proceeding":"NeurIPS 2018 12","authors":["Colin Graber","Ofer Meshi","Alexander Schwing"],"abstract":"Deep structured models are widely used for tasks like semantic segmentation,\nwhere explicit correlations between variables provide important prior\ninformation which generally helps to reduce the data needs of deep nets.\nHowever, current deep structured models are restricted by oftentimes very local\nneighborhood structure, which cannot be increased for computational complexity\nreasons, and by the fact that the output configuration, or a representation\nthereof, cannot be transformed further. Very recent approaches which address\nthose issues include graphical model inference inside deep nets so as to permit\nsubsequent non-linear output space transformations. However, optimization of\nthose formulations is challenging and not well understood. Here, we develop a\nnovel model which generalizes existing approaches, such as structured\nprediction energy networks, and discuss a formulation which maintains\napplicability of existing inference techniques.","url_abs":"http://arxiv.org/abs/1811.00539v1","url_pdf":"http://arxiv.org/pdf/1811.00539v1.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":"deep-structured-prediction-with-nonlinear","repo_url":"https://github.com/cgraber/NLStruct","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.00539","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}