{"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/image-segmentation-by-iterative-inference","title":"Image Segmentation by Iterative Inference from Conditional Score Estimation","arxiv_id":"1705.07450","date":"2017-05-21","proceeding":"ICLR 2018 1","authors":["Adriana Romero","Michal Drozdzal","Akram Erraqabi","Simon Jégou","Yoshua Bengio"],"abstract":"Inspired by the combination of feedforward and iterative computations in the\nvirtual cortex, and taking advantage of the ability of denoising autoencoders\nto estimate the score of a joint distribution, we propose a novel approach to\niterative inference for capturing and exploiting the complex joint distribution\nof output variables conditioned on some input variables. This approach is\napplied to image pixel-wise segmentation, with the estimated conditional score\nused to perform gradient ascent towards a mode of the estimated conditional\ndistribution. This extends previous work on score estimation by denoising\nautoencoders to the case of a conditional distribution, with a novel use of a\ncorrupted feedforward predictor replacing Gaussian corruption. An advantage of\nthis approach over more classical ways to perform iterative inference for\nstructured outputs, like conditional random fields (CRFs), is that it is not\nany more necessary to define an explicit energy function linking the output\nvariables. To keep computations tractable, such energy function\nparametrizations are typically fairly constrained, involving only a few\nneighbors of each of the output variables in each clique. We experimentally\nfind that the proposed iterative inference from conditional score estimation by\nconditional denoising autoencoders performs better than comparable models based\non CRFs or those not using any explicit modeling of the conditional joint\ndistribution of outputs.","url_abs":"http://arxiv.org/abs/1705.07450v2","url_pdf":"http://arxiv.org/pdf/1705.07450v2.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":"image-segmentation-by-iterative-inference","repo_url":"https://github.com/adri-romsor/iterative_inference_segm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}