{"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/semi-crowdsourced-clustering-with-deep","title":"Semi-crowdsourced Clustering with Deep Generative Models","arxiv_id":"1810.11971","date":"2018-10-29","proceeding":"NeurIPS 2018 12","authors":["Yucen Luo","Tian Tian","Jiaxin Shi","Jun Zhu","Bo Zhang"],"abstract":"We consider the semi-supervised clustering problem where crowdsourcing\nprovides noisy information about the pairwise comparisons on a small subset of\ndata, i.e., whether a sample pair is in the same cluster. We propose a new\napproach that includes a deep generative model (DGM) to characterize low-level\nfeatures of the data, and a statistical relational model for noisy pairwise\nannotations on its subset. The two parts share the latent variables. To make\nthe model automatically trade-off between its complexity and fitting data, we\nalso develop its fully Bayesian variant. The challenge of inference is\naddressed by fast (natural-gradient) stochastic variational inference\nalgorithms, where we effectively combine variational message passing for the\nrelational part and amortized learning of the DGM under a unified framework.\nEmpirical results on synthetic and real-world datasets show that our model\noutperforms previous crowdsourced clustering methods.","url_abs":"http://arxiv.org/abs/1810.11971v1","url_pdf":"http://arxiv.org/pdf/1810.11971v1.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":"semi-crowdsourced-clustering-with-deep","repo_url":"https://github.com/xinmei9322/semicrowd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1810.11971","atlas_url":"https://app.syntology.ai/?focus=1810.11971","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}