{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T02:44:27Z","timestamp":1710384267005},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2020,12,2]],"date-time":"2020-12-02T00:00:00Z","timestamp":1606867200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,2]]},"abstract":"<jats:p>Deep neural network (DNN) has shown significant improvement in learning and generalizing different machine learning tasks over the years. But it comes with an expense of heavy computational power and memory requirements. We can see that machine learning applications are even running in portable devices like mobiles and embedded systems nowadays, which generally have limited resources regarding computational power and memory and thus can only run small machine learning models. However, smaller networks usually do not perform very well. In this paper, we have implemented a simple ensemble learning based knowledge distillation network to improve the accuracy of such small models. Our experimental results prove that the performance enhancement of smaller models can be achieved through distilling knowledge from a combination of small models rather than using a cumbersome model for the knowledge transfer. Besides, the ensemble knowledge distillation network is simpler, time-efficient, and easy to implement.<\/jats:p>","DOI":"10.3233\/faia200778","type":"book-chapter","created":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T11:50:09Z","timestamp":1606996209000},"source":"Crossref","is-referenced-by-count":1,"title":["A Simple Ensemble Learning Knowledge Distillation"],"prefix":"10.3233","author":[{"given":"Himel","family":"Das Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, Texas, 79409, USA"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Bioinformatics facility of Xavier RCMI Center of Cancer Research, Xavier University of Louisiana, New Orleans, Louisiana, 70125, USA"}]},{"given":"Victor S.","family":"Sheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, Texas, 79409, USA"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA200778","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,3]],"date-time":"2020-12-03T11:50:10Z","timestamp":1606996210000},"score":1,"resource":{"primary":{"URL":"http:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA200778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,2]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia200778","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,2]]}}}