{"type":"ScholarlyArticle","title":"Beyond Labels: Knowledge Elicitation using Deep Metric Learning and Psychometric Testing","authors":[{"name":"Lu YIn","slug":"lu-yin","given":"Lu","family":"YIn","affiliation":"Eindhoven University of Technology"}],"abstract":"Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the different (parts of) molecules are organized in groups and subgroups based on their functions; plants, molecules, and astronomical objects all form complex taxonomies. Nevertheless, when applying supervised machine learning (ML) in such domains, we commonly reduce the complex and rich knowledge to a fixed set of labels. This oversimplifies and limits the potential impact that the ML solution can deliver. The main reason for such a reductionist approach is the difficulty in eliciting the domain knowledge from the experts. Developing a label structure with sufficient fidelity and providing comprehensive multi-label annotation can be exceedingly labor-intensive in many real-world applications. Here, we provide a method for efficient hierarchical knowledge elicitation (HKE) from experts working with high-dimensional data such as images or videos. Our method is based on psychometric testing and active deep metric learning. The developed models embed the high-dimensional data in a metric space where distances are semantically meaningful, and the data can be organized in a hierarchical structure.","doi":"10.24963/ijcai.2020/747","url":"https://ijcai.org/proceedings/2020/747","pdf":"https://ijcai.org/proceedings/2020/0747.pdf","markdown":"https://ijcai.org/proceedings/2020/747.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/747","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":747,"pages":{"first":5214,"last":5215},"keywords":["Humans and AI: Cognitive Modeling","Machine Learning: Semi-Supervised Learning","Machine Learning: Active Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24539/file_24539.m3u8","length_sec":275,"provider":"hls"}],"references":[{"position":1,"raw":"[Feng et al., 2014] Hsin-Chang Feng, Michael W Marcellin, and Ali Bilgin. A methodology for visually lossless jpeg2000 compression of monochrome stereo images. IEEE Transactions on Image Processing, 24(2):560-572, 2014.","title":"A methodology for visually lossless jpeg2000 compression of monochrome stereo images","authors":"Hsin-Chang Feng, Michael W Marcellin, Ali Bilgin","year":"2014","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Gescheider, 2013] George A Gescheider. Psychophysics: the fundamentals. Psychology Press, 2013.","title":"Psychophysics: the fundamentals","authors":"George A Gescheider","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Green et al., 1966] David Marvin Green, John A Swets, et al. Signal detection theory and psychophysics, vol- ume 1. Wiley New York, 1966.","title":"Signal detection theory and psychophysics","authors":"David Marvin Green, John A Swets","year":"1966","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hadizadeh and Bajić, 2013] Hadi Hadizadeh and Ivan V Bajić. Saliency-aware video compression. IEEE Trans- actions on Image Processing, 23(1):19-33, 2013.","title":"Saliency-aware video compression","authors":"Hadi Hadizadeh, Ivan V Bajić","year":"2013","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Menkovski and Liotta, 2012] Vlado Menkovski and Anto- nio Liotta. Adaptive psychometric scaling for video qual- ity assessment. Signal Processing: Image Communica- tion, 27(8):788-799, 2012.","title":"Vlado Menkovski and Antonio Liotta. Adaptive psychometric scaling for video quality assessment","authors":"Liotta Menkovski","year":"2012","venue":"Signal Processing: Image Communication","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}