{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T11:20:45Z","timestamp":1772536845452,"version":"3.50.1"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:00:00Z","timestamp":1634428800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,17]]},"DOI":"10.1109\/smc52423.2021.9659161","type":"proceedings-article","created":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T15:34:35Z","timestamp":1641483275000},"page":"2767-2774","source":"Crossref","is-referenced-by-count":14,"title":["A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification"],"prefix":"10.1109","author":[{"given":"Feras","family":"Albardi","sequence":"first","affiliation":[]},{"given":"H M Dipu","family":"Kabir","sequence":"additional","affiliation":[]},{"given":"Md Mahbub Islam","family":"Bhuiyan","sequence":"additional","affiliation":[]},{"given":"Parham M.","family":"Kebria","sequence":"additional","affiliation":[]},{"given":"Abbas","family":"Khosravi","sequence":"additional","affiliation":[]},{"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Spinalnet: Deep neural network with gradual input","author":"kabir","year":"2020"},{"key":"ref38","first-page":"87","article-title":"Jupyter notebooks ? a publishing format for reproducible computational workflows","author":"kluyver","year":"2016","journal-title":"Positioning and Power in Academic Publishing Players Agents and Agendas"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref32","article-title":"225 bird species","year":"2020","journal-title":"Kaggle"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2478\/ausi-2018-0002","article-title":"Fruit recognition from images using deep learning","volume":"10","author":"muresan","year":"2018","journal-title":"Acta Universitatis Sapientiae Informatica"},{"key":"ref30","article-title":"10 monkey species","year":"2018","journal-title":"Kaggle"},{"key":"ref37","article-title":"A linear time-varying model predictive control-based motion cueing algorithm for hexapod simulation-based motion platform","author":"qazani","year":"2019","journal-title":"IEEE Transactions on Systems Man and Cybernetics Systems"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2019.2940754"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAR.2018.8384723"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574719001516"},{"key":"ref10","article-title":"One weird trick for parallelizing convolutional neural networks","author":"krizhevsky","year":"2014"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3036090"},{"key":"ref11","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref13","article-title":"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and&#x00A1; 0.5 mb model size","author":"iandola","year":"2016"},{"key":"ref14","first-page":"4700","article-title":"Densely connected convolutional networks","author":"huang","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref28","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2016"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2836917"},{"key":"ref27","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from over-fitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref3","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3447583"},{"key":"ref29","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s40997-019-00322-y"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2019.8914597"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2019.8914458"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2017.08.081"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3006319"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.5244\/C.30.87","article-title":"Wide residual networks","author":"zagoruyko","year":"2016"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106878"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref24","article-title":"Partial adversarial training for neural network-based uncertainty quantification","author":"kabir","year":"2019","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"ref41","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"ref26","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"ICML"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"}],"event":{"name":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","location":"Melbourne, Australia","start":{"date-parts":[[2021,10,17]]},"end":{"date-parts":[[2021,10,20]]}},"container-title":["2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9658572\/9658575\/09659161.pdf?arnumber=9659161","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:56:25Z","timestamp":1652187385000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9659161\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,17]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/smc52423.2021.9659161","relation":{},"subject":[],"published":{"date-parts":[[2021,10,17]]}}}