{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:56:31Z","timestamp":1781178991767,"version":"3.54.1"},"reference-count":66,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T00:00:00Z","timestamp":1781136000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computer Graphics Forum"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Knowledge distillation is a widely used technique whereby knowledge from large pre\u2010trained models is transferred into smaller student models. However, this process is non\u2010trivial and traditionally requires technical and theoretical expertise in AI\/ML. We investigate a visualization\u2010driven strategy for making this process more accessible and intuitive via developing I\n                    <jats:sc>n<\/jats:sc>\n                    F\n                    <jats:sc>i<\/jats:sc>\n                    C\n                    <jats:sc>on<\/jats:sc>\n                    D, a novel tool that leverages visual concepts to scaffold the knowledge distillation process and support subsequent no\u2010code fine\u2010tuning of student models. I\n                    <jats:sc>n<\/jats:sc>\n                    F\n                    <jats:sc>i<\/jats:sc>\n                    C\n                    <jats:sc>on<\/jats:sc>\n                    D's backend pipeline extracts text\u2010aligned visual concepts and constructs highly interpretable student models; its frontend supports interactively fine\u2010tuning these student models by directly manipulating concept influences. Empirical evaluations help validate that I\n                    <jats:sc>n<\/jats:sc>\n                    F\n                    <jats:sc>i<\/jats:sc>\n                    C\n                    <jats:sc>on<\/jats:sc>\n                    D effectively supports knowledge distillation and subsequent fine\u2010tuning workflows. We additionally discuss insights and lessons learned about how human\u2010in\u2010the\u2010loop and visualization\u2010driven approaches like I\n                    <jats:sc>n<\/jats:sc>\n                    F\n                    <jats:sc>i<\/jats:sc>\n                    C\n                    <jats:sc>on<\/jats:sc>\n                    D can support accessible and adaptable AI explainability and model distillation.\n                  <\/jats:p>","DOI":"10.1111\/cgf.70472","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:01:14Z","timestamp":1781175674000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["I\n                    <scp>n<\/scp>\n                    F\n                    <scp>i<\/scp>\n                    C\n                    <scp>on<\/scp>\n                    D: Investigating Interactive No\u2010code Fine\u2010tuning with Concept\u2010based Knowledge Distillation"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8238-5439","authenticated-orcid":false,"given":"J.","family":"Huang","sequence":"first","affiliation":[{"name":"Arizona State University  United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5376-5803","authenticated-orcid":false,"given":"W.","family":"He","sequence":"additional","affiliation":[{"name":"Bosch Research North America  United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9138-3351","authenticated-orcid":false,"given":"L.","family":"Gou","sequence":"additional","affiliation":[{"name":"Bosch Research North America  United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1813-8844","authenticated-orcid":false,"given":"L.","family":"Ren","sequence":"additional","affiliation":[{"name":"Bosch Research North America  United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2430-815X","authenticated-orcid":false,"given":"C.","family":"Bryan","sequence":"additional","affiliation":[{"name":"Arizona State University  United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,11]]},"reference":[{"key":"e_1_2_9_2_2","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.474","article-title":"Knowledge distillation in deep learning and its applications","volume":"7","author":"Alkhulaifi A.","year":"2021","journal-title":"PeerJ Computer Science"},{"key":"e_1_2_9_3_2","unstructured":"AlballaN. CaniniM.: Practical insights into knowledge distillation for pre-trained models.arXiv preprint arXiv:2402.14922(2024). 1"},{"key":"e_1_2_9_4_2","unstructured":"AfoninA. KarimireddyS.: Towards model agnostic federated learning using knowledge distillation.arXiv preprint arXiv:2110.15210(2021). 4"},{"issue":"16","key":"e_1_2_9_5_2","doi-asserted-by":"crossref","first-page":"4172","DOI":"10.3390\/cancers15164172","article-title":"Brain tumor detection based on deep learning approaches and magnetic resonance imaging","volume":"15","author":"Abdusalomov A.","year":"2023","journal-title":"Cancers"},{"key":"e_1_2_9_6_2","unstructured":"BauD. ZhouB. KhoslaA. OlivaA. TorralbaA.: Network dissection: Quantifying interpretability of deep visual representations. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2017) pp.6541\u20136549. 5"},{"key":"e_1_2_9_7_2","unstructured":"ChoJ. HariharanB.: On the efficacy of knowledge distillation. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2019) pp.4794\u20134802. 1"},{"key":"e_1_2_9_8_2","unstructured":"ChengX. RaoZ. ChenY. ZhangQ.: Explaining knowledge distillation by quantifying the knowledge. InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(2020) pp.12925\u201312935. 1 3"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"ChuaT.-S. TangJ. HongR. LiH. LuoZ. ZhengY.: NUS-WIDE: A real-world web image database from national university of Singapore. InProceedings of the ACM International Conference on Image and Video Retrieval(2009) pp.1\u20139. 5","DOI":"10.1145\/1646396.1646452"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"CaesarH. UijlingsJ. FerrariV.: Coco-Stuff: Thing and stuff classes in context. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2018) pp.1209\u20131218. 5","DOI":"10.1109\/CVPR.2018.00132"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","first-page":"17043","DOI":"10.52202\/068431-1240","article-title":"Two-stream network for sign language recognition and translation","volume":"35","author":"Chen Y.","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"5","key":"e_1_2_9_12_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MCG.2019.2922592","article-title":"BEAMES: Interactive multimodel steering, selection, and inspection for regression tasks","volume":"39","author":"Das S.","year":"2019","journal-title":"IEEE Computer Graphics and Applications"},{"key":"e_1_2_9_13_2","unstructured":"DankarA. JassaniA. KumarK.: Improving knowledge distillation for BERT models: Loss functions mapping methods and weight tuning.arXiv preprint arXiv:2308.13958(2023). 4"},{"key":"e_1_2_9_14_2","doi-asserted-by":"crossref","unstructured":"EltonD. DasegowdaG. SatoJ. FriasE. MamonovA. WaltersM. ZiemelisM. SchultzT. BizzoB. DreyerK. et al.: No-code machine learning in radiology: Implementation and validation of a platform that allows clinicians to train their own models.medRxiv(2024) 2024\u201304. 1 4","DOI":"10.1101\/2024.04.24.24306288"},{"key":"e_1_2_9_15_2","unstructured":"EveringhamM. Van GoolL. WilliamsC. WinnJ. ZissermanA.:The PASCAL visual object classes challenge 2007 (VOC2007) results.http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2007\/workshop\/index.html. 5"},{"key":"e_1_2_9_16_2","unstructured":"EveringhamM. Van GoolL. WilliamsC. K. I. WinnJ. ZissermanA.:The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results.http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html. 5"},{"key":"e_1_2_9_17_2","unstructured":"FongR. C. VedaldiA.: Interpretable explanations of black boxes by meaningful perturbation. InProceedings of the IEEE International Conference on Computer Vision(2017) pp.3429\u20133437. 2"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"GuJ. WuZ. TrespV.: Introspective learning by distilling knowledge from online self-explanation. InProceedings of the Asian Conference on Computer Vision(2020). 3","DOI":"10.1007\/978-3-030-69538-5_3"},{"key":"e_1_2_9_19_2","article-title":"Towards automatic concept-based explanations","volume":"32","author":"Ghorbani A.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"6","key":"e_1_2_9_20_2","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou J.","year":"2021","journal-title":"International Journal of Computer Vision"},{"key":"e_1_2_9_21_2","unstructured":"HuangS.-C. CaoC.-F. LiaoP.-H. LeeL.-H. LeeP.-L. ShyuK.-K.: Enhancing Chinese multi-label text classification performance with response-based knowledge distillation. InProceedings of the 34th Conference on Computational Linguistics and Speech Processing(2022) pp.25\u201331. 6"},{"issue":"1","key":"e_1_2_9_22_2","first-page":"74","article-title":"Visual concept programming: A visual analytics approach to injecting human intelligence at scale","volume":"29","author":"Hoque M.","year":"2022","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"HeW. JamonnakS. GouL. RenL.: Clip-s4: Language\u2013guided self-supervised semantic segmentation. InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(2023) pp.11207\u201311216. 1 3 4 5","DOI":"10.1109\/CVPR52729.2023.01078"},{"issue":"1","key":"e_1_2_9_24_2","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TVCG.2022.3209384","article-title":"Conceptexplainer: Interactive explanation for deep neural networks from a concept perspective","volume":"29","author":"Huang J.","year":"2022","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"HouY. MaZ. LiuC. LoyC. C.: Learning lightweight lane detection cnns by self attention distillation. InProceedings of the IEEE\/CVF Onternational Conference on Computer Vision(2019) pp.1013\u20131021. 2","DOI":"10.1109\/ICCV.2019.00110"},{"issue":"1","key":"e_1_2_9_26_2","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1109\/TVCG.2019.2934659","article-title":"Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations","volume":"26","author":"Hohman F.","year":"2019","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_2_9_27_2","unstructured":"HintonG. VinyalsO. DeanJ.: Distilling the knowledge in a neural network.arXiv preprint arXiv:1503.02531(2015). 2"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","first-page":"33716","DOI":"10.52202\/068431-2443","article-title":"Knowledge distillation from a stronger teacher","volume":"35","author":"Huang T.","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_29_2","article-title":"Knowledge diffusion for distillation","volume":"36","author":"Huang T.","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"JinX. PengB. WuY. LiuY. LiuJ. LiangD. YanJ. HuX.: Knowledge distillation via route constrained optimization. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2019) pp.1345\u20131354. 2","DOI":"10.1109\/ICCV.2019.00143"},{"key":"e_1_2_9_31_2","article-title":"Paraphrasing complex network: Network compression via factor transfer","volume":"31","author":"Kim J.","year":"2018","journal-title":"Advances in Neural Information Processing systems"},{"key":"e_1_2_9_32_2","first-page":"2668","volume-title":"International Conference on Machine Learning","author":"Kim B.","year":"2018"},{"issue":"1","key":"e_1_2_9_33_2","first-page":"29","article-title":"A survey of data-driven and knowledge-aware explainable ai","volume":"34","author":"Li X.-H.","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_9_34_2","doi-asserted-by":"crossref","unstructured":"LiuH.-I. GalindoM. XieH. WongL.-K. ShuaiH.-H. LiY.-H. ChengW.-H.: Lightweight deep learning for resource-constrained environments: A survey.ACM Computing Surveys(2024). 1","DOI":"10.1145\/3657282"},{"key":"e_1_2_9_35_2","unstructured":"LeeS. H. KimD. H. SongB. C.: Self-supervised knowledge distillation using singular value decomposition. InProceedings of the European Conference on Computer Vision(2018) pp.335\u2013350. 2"},{"key":"e_1_2_9_36_2","doi-asserted-by":"crossref","unstructured":"LinT.-Y. MaireM. BelongieS. HaysJ. PeronaP. RamananD. Doll\u00e1rP. ZitnickC.: Microsoft COCO: Common objects in context. InEuropean Conference on Computer Vision(2014) pp.740\u2013755. 5","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_2_9_37_2","unstructured":"LiuS. ZhangL. YangX. SuH. ZhuJ.: Query2label: A simple transformer way to multi-label classification.arXiv preprint arXiv:2107.10834(2021). 5"},{"issue":"7","key":"e_1_2_9_38_2","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee S.","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_9_39_2","doi-asserted-by":"crossref","unstructured":"MottaghiR. ChenX. LiuX. ChoN.-G. LeeS.-W. FidlerS. UrtasunR. YuilleA.: The role of context for object detection and semantic segmentation in the wild. InIEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2014). 5","DOI":"10.1109\/CVPR.2014.119"},{"key":"e_1_2_9_40_2","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1609\/aaai.v34i04.5963","volume":"34","author":"Mirzadeh S.","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"e_1_2_9_41_2","article-title":"When does label smoothing help?","volume":"32","author":"M\u00fcller R.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_42_2","doi-asserted-by":"crossref","unstructured":"MadaioM. StarkL. Wortman VaughanJ. WallachH.: Co-designing checklists to understand organizational challenges and opportunities around fairness in ai. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems(2020) pp.1\u201314. 10","DOI":"10.1145\/3313831.3376445"},{"key":"e_1_2_9_43_2","unstructured":"OpenAI:Clip: Connecting text and images.https:\/\/openai.com\/research\/clip. 1"},{"issue":"1","key":"e_1_2_9_44_2","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1109\/TVCG.2021.3114858","article-title":"Neurocartography: Scalable automatic visual summarization of concepts in deep neural networks","volume":"28","author":"Park H.","year":"2021","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"e_1_2_9_45_2","doi-asserted-by":"crossref","unstructured":"PerazziF. Pont-TusetJ. McWilliamsB. Van GoolL. GrossM. Sorkine-HornungA.: A benchmark dataset and evaluation methodology for video object segmentation. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2016) pp.724\u2013732. 5","DOI":"10.1109\/CVPR.2016.85"},{"key":"e_1_2_9_46_2","unstructured":"PassalisN. TzelepiM. TefasA.: Heterogeneous knowledge distillation using information flow modeling. InProceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition(2020) pp.2339\u20132348. 2 3"},{"key":"e_1_2_9_47_2","doi-asserted-by":"crossref","unstructured":"RongY. LeemannT. NguyenT.-T. FiedlerL. QianP. UnhelkarV. SeidelT. KasneciG. KasneciE.: Towards human-centered explainable AI: A survey of user studies for model explanations.IEEE Transactions on Pattern Analysis and Machine Intelligence(2023). 4","DOI":"10.1109\/TPAMI.2023.3331846"},{"key":"e_1_2_9_48_2","doi-asserted-by":"crossref","unstructured":"ShinH. ChoiD.-W.: Teacher as a lenient expert: Teacher-agnostic data-free knowledge distillation.arXiv preprint arXiv:2402.12406(2024). 4","DOI":"10.1609\/aaai.v38i13.29420"},{"issue":"12","key":"e_1_2_9_49_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3390\/fi14120363","article-title":"TinyML for ultra-low power AI and large scale IoT deployments: A systematic review","volume":"14","author":"Schizas N.","year":"2022","journal-title":"Future Internet"},{"key":"e_1_2_9_50_2","unstructured":"SousaJ. MoreiraR. BalayanV. SaleiroP. BizarroP.: ConceptDistil: Model-agnostic distillation of concept explanations.arXiv preprint arXiv:2205.03601(2022). 1"},{"key":"e_1_2_9_51_2","doi-asserted-by":"crossref","unstructured":"SonW. NaJ. ChoiJ. HwangW.: Densely guided knowledge distillation using multiple teacher assistants. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2021) pp.9395\u20139404. 4","DOI":"10.1109\/ICCV48922.2021.00926"},{"key":"e_1_2_9_52_2","doi-asserted-by":"crossref","unstructured":"TullioJ. DeyA. ChaleckiJ. FogartyJ.: How it works: a field study of non-technical users interacting with an intelligent system. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(2007) pp.31\u201340. 8","DOI":"10.1145\/1240624.1240630"},{"key":"e_1_2_9_53_2","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.ins.2017.12.034","article-title":"Multi-label classification using a fuzzy rough neighborhood consensus","volume":"433","author":"Vluymans S.","year":"2018","journal-title":"Information Sciences"},{"issue":"4","key":"e_1_2_9_54_2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s11633-022-1410-8","article-title":"Large-scale multi-modal pre-trained models: A comprehensive survey","volume":"20","author":"Wang X.","year":"2023","journal-title":"Machine Intelligence Research"},{"key":"e_1_2_9_55_2","doi-asserted-by":"crossref","unstructured":"WangQ. L'YiS. GehlenborgN.: Drava: Aligning human concepts with machine learning latent dimensions for the visual exploration of small multiples. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(2023). 3","DOI":"10.1145\/3544548.3581127"},{"issue":"6","key":"e_1_2_9_56_2","doi-asserted-by":"crossref","first-page":"3048","DOI":"10.1109\/TPAMI.2021.3055564","article-title":"Knowledge distillation and student-teacher learning for visual intelligence: A review and new outlooks","volume":"44","author":"Wang L.","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_9_57_2","unstructured":"YangP. XieM.-K. ZongC.-C. FengL. NiuG. SugiyamaM. HuangS.-J.: Multi-label knowledge distillation. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2023) pp.17271\u201317280. 5"},{"key":"e_1_2_9_58_2","doi-asserted-by":"crossref","unstructured":"YangC. YuX. AnZ. XuY.: Categories of response-based feature-based and relation-based knowledge distillation. InAdvancements in Knowledge Distillation: Towards New Horizons of Intelligent Systems.2023 pp.1\u201332. 6","DOI":"10.1007\/978-3-031-32095-8_1"},{"issue":"4","key":"e_1_2_9_59_2","first-page":"5099","article-title":"Quantifying the knowledge in a DNN to explain knowledge distillation for classification","volume":"45","author":"Zhang Q.","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_2_9_60_2","doi-asserted-by":"crossref","unstructured":"ZhouJ. ChenF. HolzingerA.: Towards explainability for AI fairness. InInternational Workshop on Extending Explainable AI Beyond Deep Models and Classifiers(2020) pp.375\u2013386. 10","DOI":"10.1007\/978-3-031-04083-2_18"},{"issue":"3","key":"e_1_2_9_61_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"111","author":"Zou Z.","year":"2023","journal-title":"Proceedings of the IEEE"},{"key":"e_1_2_9_62_2","article-title":"Knowledge distillation by on-the-fly native ensemble","volume":"31","author":"Zhu X.","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_63_2","unstructured":"ZagoruykoS. KomodakisN.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer.arXiv preprint arXiv:1612.03928(2016). 3"},{"key":"e_1_2_9_64_2","first-page":"32011","article-title":"Teach less, learn more: On the undistillable classes in knowledge distillation","volume":"35","author":"Zhu Y.","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_65_2","unstructured":"ZhangY. QinY. LiuH. ZhangY. LiY. GuX.: Knowledge distillation from single to multi labels: An empirical study.arXiv preprint arXiv:2303.08360(2023). 5"},{"key":"e_1_2_9_66_2","unstructured":"ZhangL. SongJ. GaoA. ChenJ. BaoC. MaK.: Be your own teacher: Improve the performance of convolutional neural networks via self distillation. InProceedings of the IEEE\/CVF International Conference on Computer Vision(2019) pp.3713\u20133722. 2"},{"issue":"1","key":"e_1_2_9_67_2","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/TVCG.2021.3114837","article-title":"Human-in-the-loop extraction of interpretable concepts in deep learning models","volume":"28","author":"Zhao Z.","year":"2021","journal-title":"IEEE Transactions on Visualization and Computer Graphics"}],"container-title":["Computer Graphics Forum"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cgf.70472","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/cgf.70472","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/cgf.70472","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T11:01:38Z","timestamp":1781175698000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/cgf.70472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,11]]},"references-count":66,"alternative-id":["10.1111\/cgf.70472"],"URL":"https:\/\/doi.org\/10.1111\/cgf.70472","archive":["Portico"],"relation":{},"ISSN":["0167-7055","1467-8659"],"issn-type":[{"value":"0167-7055","type":"print"},{"value":"1467-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,11]]},"assertion":[{"value":"2026-06-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70472"}}