{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:42:04Z","timestamp":1742949724226,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030921842"},{"type":"electronic","value":"9783030921859"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92185-9_8","type":"book-chapter","created":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T17:02:46Z","timestamp":1638723766000},"page":"91-102","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Genetic Algorithm and\u00a0Distinctiveness Pruning in\u00a0the\u00a0Shallow Networks for\u00a0VehicleX"],"prefix":"10.1007","author":[{"given":"Linwei","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yeu-Shin","family":"Fu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"unstructured":"Buhrmester, V., M\u00fcnch, D., Arens, M.: Analysis of explainers of black box deep neural networks for computer vision: A survey. arXiv preprint arXiv:1911.12116 (2019)","key":"8_CR1"},{"doi-asserted-by":"crossref","unstructured":"Chandakkar, P.S., Li, Y., Ding, P.L.K., Li, B.: Strategies for re-training a pruned neural network in an edge computing paradigm. In: 2017 IEEE International Conference on Edge Computing (EDGE), pp. 244\u2013247. IEEE (2017)","key":"8_CR2","DOI":"10.1109\/IEEE.EDGE.2017.45"},{"doi-asserted-by":"crossref","unstructured":"El-Maaty, A.M.A., Wassal, A.G.: Hybrid GA-PCA feature selection approach for inertial human activity recognition. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1027\u20131032. IEEE (2018)","key":"8_CR3","DOI":"10.1109\/SSCI.2018.8628702"},{"doi-asserted-by":"crossref","unstructured":"Gedeon, T.D.: Indicators of hidden neuron functionality: the weight matrix versus neuron behaviour. In: Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, pp. 26\u201329. IEEE (1995)","key":"8_CR4","DOI":"10.1109\/ANNES.1995.499431"},{"unstructured":"Gedeon, T.D., Harris, D.: Network reduction techniques. In: Proceedings International Conference on Neural Networks Methodologies and Applications, vol. 1, pp. 119\u2013126 (1991)","key":"8_CR5"},{"issue":"3","key":"8_CR6","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1109\/TCAD.2011.2170568","volume":"31","author":"E Kakoulli","year":"2012","unstructured":"Kakoulli, E., Soteriou, V., Theocharides, T.: Intelligent hotspot prediction for network-on-chip-based multicore systems. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 31(3), 418\u2013431 (2012)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"doi-asserted-by":"crossref","unstructured":"Kim, E., Gopinath, D., Pasareanu, C., Seshia, S.A.: A programmatic and semantic approach to explaining and debugging neural network based object detectors. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11128\u201311137 (2020)","key":"8_CR7","DOI":"10.1109\/CVPR42600.2020.01114"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)","key":"8_CR8"},{"unstructured":"Li, B., Zhang, T., Xia, T.: Vehicle detection from 3d lidar using fully convolutional network. arXiv preprint arXiv:1608.07916 (2016)","key":"8_CR9"},{"unstructured":"Nowruzi, F.E., Kapoor, P., Kolhatkar, D., Hassanat, F.A., Laganiere, R., Rebut, J.: How much real data do we actually need: Analyzing object detection performance using synthetic and real data. arXiv preprint arXiv:1907.07061 (2019)","key":"8_CR10"},{"issue":"1","key":"8_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.patcog.2006.04.041","volume":"40","author":"G Ou","year":"2007","unstructured":"Ou, G., Murphey, Y.L.: Multi-class pattern classification using neural networks. Pattern Recogn. 40(1), 4\u201318 (2007)","journal-title":"Pattern Recogn."},{"issue":"3","key":"8_CR12","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","volume":"32","author":"JD Rodriguez","year":"2009","unstructured":"Rodriguez, J.D., Perez, A., Lozano, J.A.: Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 569\u2013575 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Sehgal, S., Singh, H., Agarwal, M., Bhasker, V., et al.: Data analysis using principal component analysis. In: 2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems (MedCom), pp. 45\u201348. IEEE (2014)","key":"8_CR13","DOI":"10.1109\/MedCom.2014.7005973"},{"issue":"12","key":"8_CR14","first-page":"310","volume":"6","author":"S Sharma","year":"2017","unstructured":"Sharma, S., Sharma, S.: Activation functions in neural networks. Towards DataScience 6(12), 310\u2013316 (2017)","journal-title":"Towards DataScience"},{"doi-asserted-by":"crossref","unstructured":"Tang, Z., et al.: Pamtri: pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. In: Proceedings of the IEEE\/CVF Inter-national Conference on Computer Vision, pp. 211\u2013220 (2019)","key":"8_CR15","DOI":"10.1109\/ICCV.2019.00030"},{"key":"8_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/978-3-030-58539-6_46","volume-title":"Computer Vision","author":"Y Yao","year":"2020","unstructured":"Yao, Y., Zheng, L., Yang, X., Naphade, M., Gedeon, T.: Simulating content consistent vehicle datasets with attribute descent. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12351, pp. 775\u2013791. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_46"},{"unstructured":"Zhu, M., Gupta, S.: To prune, or not to prune: exploring the efficacy of pruning for model compression. arXiv preprint arXiv:1710.01878 (2017)","key":"8_CR17"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92185-9_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T20:08:21Z","timestamp":1726258101000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92185-9_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030921842","9783030921859"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92185-9_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"177","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.57","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}