{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:39:29Z","timestamp":1777390769985,"version":"3.51.4"},"reference-count":72,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Project of Chinese academy of engineering ``The Online and Offline Mixed Educational Service System for 'The Belt and Road' Training in MOOC China''"},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No.2020AAA0108800"],"award-info":[{"award-number":["No.2020AAA0108800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Research Team of Ministry of Education","award":["IRT_17R86"],"award-info":[{"award-number":["IRT_17R86"]}]},{"name":"Project of China Knowledge Center for Engineering Science and Technology"},{"name":"Innovative Research Group of the National Natural Science Foundation of China","award":["No.61721002"],"award-info":[{"award-number":["No.61721002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61872287"],"award-info":[{"award-number":["No.61872287"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61937001"],"award-info":[{"award-number":["No.61937001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62050194"],"award-info":[{"award-number":["No.62050194"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62137002"],"award-info":[{"award-number":["No.62137002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62192781"],"award-info":[{"award-number":["No.62192781"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CCF-AFSG Research Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tip.2024.3404663","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T18:01:21Z","timestamp":1717092081000},"page":"3520-3535","source":"Crossref","is-referenced-by-count":10,"title":["Disentangled Generation With Information Bottleneck for Enhanced Few-Shot Learning"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3775-3722","authenticated-orcid":false,"given":"Zhuohang","family":"Dang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, the Ministry of Education Key Laboratory of Intelligent Networks and Network Security, and the Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0140-7860","authenticated-orcid":false,"given":"Minnan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, the Ministry of Education Key Laboratory of Intelligent Networks and Network Security, and the Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8153-6899","authenticated-orcid":false,"given":"Jihong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, the Ministry of Education Key Laboratory of Intelligent Networks and Network Security, and the Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6921-0303","authenticated-orcid":false,"given":"Chengyou","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, the Ministry of Education Key Laboratory of Intelligent Networks and Network Security, and the Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2595-0398","authenticated-orcid":false,"given":"Caixia","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, the Ministry of Education Key Laboratory of Intelligent Networks and Network Security, and the Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang","family":"Dai","sequence":"additional","affiliation":[{"name":"SGIT AI Laboratory, Xi&#x2019;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7778-8807","authenticated-orcid":false,"given":"Xiaojun","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8436-4754","authenticated-orcid":false,"given":"Qinghua","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, the Ministry of Education Key Laboratory of Intelligent Networks and Network Security, and the Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Adaptive cross-modal few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xing"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20044-1_17"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01348"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00401"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00880"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3241651"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01060"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00869"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3241919"},{"key":"ref10","first-page":"2734","article-title":"Interventional few-shot learning","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Yue"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16967"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17120"},{"key":"ref13","article-title":"The information bottleneck method","author":"Tishby","year":"2000","journal-title":"arXiv:physics\/0004057"},{"key":"ref14","first-page":"17148","article-title":"Distilling robust and non-robust features in adversarial examples by information bottleneck","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kim"},{"key":"ref15","first-page":"1","article-title":"Learning structured output representation using deep conditional generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sohn"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2959254"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3046861"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3192712"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3228162"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3124322"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3239197"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_16"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01069"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"ref25","first-page":"24474","article-title":"FeLMi: Few shot learning with hard mixup","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","volume":"35","author":"Roy"},{"key":"ref26","first-page":"1","article-title":"beta-VAE: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Higgins"},{"key":"ref27","first-page":"1","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref28","first-page":"1","article-title":"Deep variational information bottleneck","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Alemi"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00087"},{"key":"ref30","first-page":"1779","article-title":"CLUB: A contrastive log-ratio upper bound of mutual information","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Cheng"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01259"},{"key":"ref32","first-page":"2649","article-title":"Disentangling by factorising","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00087"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref35","article-title":"Dizygotic conditional variational AutoEncoder for multi-modal and partial modality absent few-shot learning","author":"Zhang","year":"2021","journal-title":"arXiv:2106.14467"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00375"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01040"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20044-1_1"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00957"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00870"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3217373"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20044-1_26"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01401"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00887"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3142530"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3184813"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3143692"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02299"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00727"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02298"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2910052"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00053"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.02258"},{"key":"ref54","first-page":"1","article-title":"Zero-shot learning via simultaneous generating and learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yu"},{"key":"ref55","first-page":"10991","article-title":"Bridging multi-task learning and meta-learning: Towards efficient training and effective adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00236"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00857"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00832"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_41"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.3390\/e21121181"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.3390\/e22010098"},{"key":"ref62","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref63","first-page":"1","article-title":"Disentangling factors of variation in deep representation using adversarial training","volume-title":"Proc. NIPS","volume":"29","author":"Mathieu"},{"key":"ref64","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014","journal-title":"arXiv:1412.6572"},{"key":"ref65","first-page":"1","article-title":"Adversarial examples are not bugs, they are features","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ilyas"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00129"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6130"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICEIEC49280.2020.9152261"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00781"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.14711\/thesis-991012636368103412"},{"key":"ref71","article-title":"Meta-DM: Applications of diffusion models on few-shot learning","author":"Hu","year":"2023","journal-title":"arXiv:2305.08092"},{"key":"ref72","article-title":"Towards explanation for unsupervised graph-level representation learning","author":"Zheng","year":"2022","journal-title":"arXiv:2205.09934"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/83\/10346232\/10542660.pdf?arnumber=10542660","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T05:15:25Z","timestamp":1717823725000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10542660\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":72,"URL":"https:\/\/doi.org\/10.1109\/tip.2024.3404663","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}