{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:27:23Z","timestamp":1750220843401,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,10,22]],"date-time":"2019-10-22T00:00:00Z","timestamp":1571702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,10,22]]},"DOI":"10.1145\/3331453.3361326","type":"proceedings-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T13:34:22Z","timestamp":1571664862000},"page":"1-7","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of Metaheuristic Algorithms for Determining the Structure of a Convolutional Neural Network with a Small Dataset"],"prefix":"10.1145","author":[{"given":"Po-Chou","family":"Shih","sequence":"first","affiliation":[{"name":"Business school, University of Shanghai for Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Business school, University of Shanghai for Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Lin","sequence":"additional","affiliation":[{"name":"Business school, University of Shanghai for Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Business school, University of Shanghai for Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,10,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Very deep convolutional networks for large-scale Image Recognition. ICLR, 1--14","author":"Simonyan K","year":"2015","unstructured":"K Simonyan and A Zisserman ( 2015 ). Very deep convolutional networks for large-scale Image Recognition. ICLR, 1--14 . K Simonyan and A Zisserman (2015). Very deep convolutional networks for large-scale Image Recognition. ICLR, 1--14."},{"key":"e_1_3_2_1_2_1","volume-title":"I Sutskever and G E Hinton","author":"Krizhevsky A","year":"2012","unstructured":"A Krizhevsky , I Sutskever and G E Hinton ( 2012 ). ImageNet classification with deep convolutional neural networks. NIPS , 1106--1114. A Krizhevsky, I Sutskever and G E Hinton (2012). ImageNet classification with deep convolutional neural networks. NIPS, 1106--1114."},{"key":"e_1_3_2_1_3_1","volume-title":"U C Berkeley and J Malik","author":"Girshick R","year":"2015","unstructured":"R Girshick , J Donahue , T Darrell , U C Berkeley and J Malik ( 2015 ). Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR , 580--587, June 2014. R Girshick, J Donahue, T Darrell, U C Berkeley and J Malik (2015). Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR, 580--587, June 2014."},{"key":"e_1_3_2_1_4_1","volume-title":"R Girshick and A Farhadi","author":"Redmon J","year":"2016","unstructured":"J Redmon , S Divvala , R Girshick and A Farhadi ( 2016 ). You only look once: unified, real-Time object detection. CVPR , 779--788. J Redmon, S Divvala, R Girshick and A Farhadi (2016). You only look once: unified, real-Time object detection. CVPR, 779--788."},{"key":"e_1_3_2_1_5_1","volume-title":"Y Yang and L Zhang","author":"Sung F","year":"2018","unstructured":"F Sung , Y Yang and L Zhang ( 2018 ). Learning to compare: relation network for few-shot learning. CVPR , 1199--1208. F Sung, Y Yang and L Zhang (2018). Learning to compare: relation network for few-shot learning. CVPR, 1199--1208."},{"key":"e_1_3_2_1_6_1","volume-title":"The Effectiveness of Data Augmentation in Image Classification using Deep Learning","author":"Perez L","year":"2017","unstructured":"L Perez and J Wang ( 2017 ). The Effectiveness of Data Augmentation in Image Classification using Deep Learning , http:\/\/arxiv.org\/abs\/1712.04621. L Perez and J Wang (2017). The Effectiveness of Data Augmentation in Image Classification using Deep Learning, http:\/\/arxiv.org\/abs\/1712.04621."},{"issue":"8","key":"e_1_3_2_1_7_1","first-page":"1","article-title":"Data augmentation using random image cropping and patching for deep CNNs","volume":"14","author":"Takahashi R","year":"2015","unstructured":"R Takahashi , T Matsubara and K Uehara ( 2015 ). Data augmentation using random image cropping and patching for deep CNNs . Journal of Latex class Files , 14 ( 8 ), 1 -- 11 . R Takahashi, T Matsubara and K Uehara (2015). Data augmentation using random image cropping and patching for deep CNNs. Journal of Latex class Files, 14(8), 1--11.","journal-title":"Journal of Latex class Files"},{"key":"e_1_3_2_1_8_1","volume-title":"ICLR","author":"Ravi S","year":"2017","unstructured":"S Ravi and H Larochelle ( 2017 ). Optimization as a model for few-short learning . ICLR , Toulon, France , Apr. 2017. S Ravi and H Larochelle (2017). Optimization as a model for few-short learning. ICLR, Toulon, France, Apr. 2017."},{"key":"e_1_3_2_1_9_1","volume-title":"July","author":"Caruana R","year":"1995","unstructured":"R Caruana ( 1995 ). Learning many related tasks at the same time with back-propagation. Adv. Neural Inf. Process, 657--664 , July 1995. R Caruana (1995). Learning many related tasks at the same time with back-propagation. Adv. Neural Inf. Process, 657--664, July 1995."},{"key":"e_1_3_2_1_10_1","volume-title":"Workshop and Conference Proceedings, 27","author":"Bengio Y","year":"2012","unstructured":"Y Bengio ( 2012 ). Deep learning of representations for unsupervised and transfer Learning . Workshop and Conference Proceedings, 27 , 17--37. Y Bengio (2012). Deep learning of representations for unsupervised and transfer Learning. Workshop and Conference Proceedings, 27, 17--37."},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the 31st International Conference on Machine Learning","author":"Donahue J","year":"2014","unstructured":"J Donahue , Y Jia , O Vinyals , J Hoffman , N Zhang , E Tzeng and T Darrell ( 2014 ). DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition . Proceedings of the 31st International Conference on Machine Learning , Beijing, China , 2014. J Donahue, Y Jia, O Vinyals, J Hoffman, N Zhang, E Tzeng and T Darrell (2014). DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014."},{"key":"e_1_3_2_1_12_1","volume-title":"NIPS","author":"Yosinski J","year":"2014","unstructured":"J Yosinski , J Clune , Y Bengio and H Lipson ( 2014 ). How transferable are features in deep neural networks? . NIPS , Montreal, Canada , 2014. J Yosinski, J Clune, Y Bengio and H Lipson (2014). How transferable are features in deep neural networks?. NIPS, Montreal, Canada, 2014."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2013.6706969"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2018.8516989"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470640425"},{"key":"e_1_3_2_1_17_1","volume-title":"J H Kim and G V Loganathan","author":"Geem Z W","year":"2001","unstructured":"Z W Geem , J H Kim and G V Loganathan ( 2001 ). A New Heuristic Optimization Algorithm: Harmony Search . Simulation ( 2001), 76, 60--68. Z W Geem, J H Kim and G V Loganathan (2001). A New Heuristic Optimization Algorithm: Harmony Search. Simulation (2001), 76, 60--68."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-012-9054-5"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25751-8_82"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/1537325"},{"key":"e_1_3_2_1_21_1","volume-title":"Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks","author":"Fukushima K","year":"1988","unstructured":"K Fukushima ( 1988 ). Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks (1988), 1(2), 119--130. K Fukushima (1988). Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks (1988), 1(2), 119--130."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2004.09.007"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2006.11.033"},{"key":"e_1_3_2_1_25_1","volume-title":"M F Tasgetiren and J J Liang","author":"Pan Q K","year":"2010","unstructured":"Q K Pan , P N Suganthan , M F Tasgetiren and J J Liang ( 2010 ). A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl. Math. Comput . (2010), 216, 830--848. Q K Pan, P N Suganthan, M F Tasgetiren and J J Liang (2010). A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl. Math. Comput. (2010), 216, 830--848."},{"issue":"337","key":"e_1_3_2_1_26_1","first-page":"1","article-title":"A dynamic adjusting novel global harmony search for continuous optimization problems","volume":"10","author":"Chiu C Y","year":"2018","unstructured":"C Y Chiu , P C Shih and X Li ( 2018 ). A dynamic adjusting novel global harmony search for continuous optimization problems . Symmetry (Basel) , 10 ( 337 ), 1 -- 31 . C Y Chiu, P C Shih and X Li (2018). A dynamic adjusting novel global harmony search for continuous optimization problems. Symmetry (Basel), 10(337), 1--31.","journal-title":"Symmetry (Basel)"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2010.04.070"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10589-009-9261-6"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/1554830"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2014.01.086"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1080\/0305215X.2013.832237"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2016.01.008"}],"event":{"name":"CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering","acronym":"CSAE 2019","location":"Sanya China"},"container-title":["Proceedings of the 3rd International Conference on Computer Science and Application Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3331453.3361326","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3331453.3361326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:22:56Z","timestamp":1750202576000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3331453.3361326"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,22]]},"references-count":32,"alternative-id":["10.1145\/3331453.3361326","10.1145\/3331453"],"URL":"https:\/\/doi.org\/10.1145\/3331453.3361326","relation":{},"subject":[],"published":{"date-parts":[[2019,10,22]]},"assertion":[{"value":"2019-10-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}