{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:27:57Z","timestamp":1740122877021,"version":"3.37.3"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,1,31]],"date-time":"2018-01-31T00:00:00Z","timestamp":1517356800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1007\/s11042-018-5702-5","type":"journal-article","created":{"date-parts":[[2018,1,31]],"date-time":"2018-01-31T03:07:13Z","timestamp":1517368033000},"page":"197-211","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep networks with non-static activation function"],"prefix":"10.1007","volume":"78","author":[{"given":"Huajun","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5341-5985","authenticated-orcid":false,"given":"Zechao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,1,31]]},"reference":[{"key":"5702_CR1","unstructured":"Agostinelli F, Hoffman MD, Sadowski PJ, Baldi P (2015) Learning activation functions to improve deep neural networks. In: ICLR"},{"key":"5702_CR2","unstructured":"Chang JR, Chen YS (2015) Batch-normalized maxout network in network. arXiv: 1511.02583 1511.02583"},{"key":"5702_CR3","unstructured":"Clevert DA, Unterthiner T, Hochreiter S (2016) Fast and accurate deep network learning by exponential linear units. In: ICLR"},{"key":"5702_CR4","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: AISTATS"},{"key":"5702_CR5","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: AISTATS"},{"key":"5702_CR6","unstructured":"Goodfellow IJ, Warde-Farley D, Mirza M, Courville AC, Bengio Y (2013) Maxout networks. In: ICML"},{"key":"5702_CR7","doi-asserted-by":"crossref","unstructured":"Gulcehre C, Cho K, Pascanu R, Bengio Y (2014) Learned-norm pooling for deep feedforward and recurrent neural networks. In: ECML","DOI":"10.1007\/978-3-662-44848-9_34"},{"key":"5702_CR8","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: ICCV, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"5702_CR9","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5702_CR10","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML"},{"key":"5702_CR11","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 675\u2013678","DOI":"10.1145\/2647868.2654889"},{"key":"5702_CR12","unstructured":"Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images"},{"key":"5702_CR13","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: NIPS"},{"issue":"11","key":"5702_CR14","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"5702_CR15","unstructured":"Lee CY, Xie S, Gallagher PW, Zhang Z, Tu Z (2015) Deeply-supervised nets. In: AISTATS"},{"issue":"11","key":"5702_CR16","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1109\/TMM.2015.2477035","volume":"17","author":"Z Li","year":"2015","unstructured":"Li Z, Tang J (2015) Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans Multimed 17(11):1989\u20131999","journal-title":"IEEE Trans Multimed"},{"issue":"1","key":"5702_CR17","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TIP.2016.2624140","volume":"26","author":"Z Li","year":"2017","unstructured":"Li Z, Tang J (2017) Weakly supervised deep matrix factorization for social image understanding. IEEE Trans Image Process 26(1):276\u2013288","journal-title":"IEEE Trans Image Process"},{"key":"5702_CR18","doi-asserted-by":"crossref","unstructured":"Liang M, Hu X (2015) Recurrent convolutional neural network for object recognition. In: CVPR","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"5702_CR19","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. In ICLR"},{"key":"5702_CR20","unstructured":"Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: ICML, vol 30"},{"issue":"2605","key":"5702_CR21","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2017","unstructured":"Maaten LVD, Hinton G (2017) Visualizing data using t-sne. JMLR 9 (2605):2579\u20132605","journal-title":"JMLR"},{"key":"5702_CR22","unstructured":"Mishkin D, Matas J (2016) All you need is a good init. In: ICLR"},{"key":"5702_CR23","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML"},{"key":"5702_CR24","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y Fitnets: Hints for thin deep nets. In: ICLR"},{"key":"5702_CR25","doi-asserted-by":"crossref","unstructured":"Shang W, Sohn K, Almeida D, Lee H (2016) Understanding and improving convolutional neural networks via concatenated rectified linear units. In: ICML","DOI":"10.1609\/aaai.v31i1.10759"},{"key":"5702_CR26","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICLR"},{"key":"5702_CR27","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. JMLR 15:1929\u20131958","journal-title":"JMLR"},{"key":"5702_CR28","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. arXiv: 1505.00387"},{"key":"5702_CR29","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: CVPR, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"99","key":"5702_CR30","first-page":"1","volume":"PP","author":"Q Wang","year":"2017","unstructured":"Wang Q, Gao J, Yuan Y (2017) A joint convolutional neural networks and context transfer for street scenes labeling. IEEE Trans Intell Transp Syst PP(99):1\u201314","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"99","key":"5702_CR31","first-page":"1","volume":"PP","author":"Q Wang","year":"2017","unstructured":"Wang Q, Wan J, Yuan Y (2017) Deep metric learning for crowdedness regression. IEEE Trans Circuits Syst Video Technol PP(99):1\u20131","journal-title":"IEEE Trans Circuits Syst Video Technol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-018-5702-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-5702-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-018-5702-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T14:42:35Z","timestamp":1660401755000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-018-5702-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,31]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["5702"],"URL":"https:\/\/doi.org\/10.1007\/s11042-018-5702-5","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2018,1,31]]},"assertion":[{"value":"1 August 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}