{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:04:55Z","timestamp":1740107095800,"version":"3.37.3"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:00:00Z","timestamp":1619136000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:00:00Z","timestamp":1619136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502358"],"award-info":[{"award-number":["61502358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s00371-021-02125-2","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T23:12:39Z","timestamp":1619133159000},"page":"2489-2498","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Weight correlation reduction and features normalization: improving the performance for shallow networks"],"prefix":"10.1007","volume":"38","author":[{"given":"Can","family":"Song","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7001-5775","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,23]]},"reference":[{"issue":"10","key":"2125_CR1","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1097\/00004647-200110000-00001","volume":"21","author":"D Attwell","year":"2001","unstructured":"Attwell, D., Laughlin, S.B.: An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21(10), 1133\u20131145 (2001)","journal-title":"J. Cereb. Blood Flow Metab."},{"key":"2125_CR2","doi-asserted-by":"crossref","unstructured":"Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Noise reduction in speech processing, pp. 1\u20134. Springer (2009)","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"2125_CR3","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., Yan, Y.: BlendMask: Top-down meets bottom-up for instance segmentation. In: Computer Vision and Pattern Recognition, pp. 8573\u20138581 (2020)","DOI":"10.1109\/CVPR42600.2020.00860"},{"issue":"4","key":"2125_CR4","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018)","journal-title":"Pattern Anal. Mach. Intell."},{"key":"2125_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv preprint arXiv:1802.02611 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2125_CR6","doi-asserted-by":"crossref","unstructured":"Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: YOLO-face: a real-time face detector. Vis. Comput. 1\u20139 (2020)","DOI":"10.1007\/s00371-020-01831-7"},{"key":"2125_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, H., Xu, B., Yan, Z., Kalantidis, Y., Rohrbach, M., Yan, S., Feng, J.: Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. In: International Conference on Computer Vision, pp. 3435\u20133444 (2019)","DOI":"10.1109\/ICCV.2019.00353"},{"key":"2125_CR8","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Yu, F.X., Feris, R.S., Kumar, S., Choudhary, A., Chang, S.F.: An exploration of parameter redundancy in deep networks with circulant projections. In: International Conference on Computer Vision, pp. 2857\u20132865 (2015)","DOI":"10.1109\/ICCV.2015.327"},{"key":"2125_CR9","unstructured":"Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: the International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, pp. 215\u2013223 (2011)"},{"key":"2125_CR10","unstructured":"Denil, M., Shakibi, B., Dinh, L., Ranzato, M.A., De Freitas, N., 2013. Predicting parameters in deep learning. arXiv preprint arXiv:1306.0543 (2013)"},{"issue":"7","key":"2125_CR11","doi-asserted-by":"publisher","first-page":"2280","DOI":"10.1007\/s10489-020-01655-x","volume":"50","author":"S Ding","year":"2020","unstructured":"Ding, S., Sun, Y., An, Y., Jia, W.: Multiple birth support vector machine based on recurrent neural networks. Appl. Intell. 50(7), 2280\u20132292 (2020)","journal-title":"Appl. Intell."},{"key":"2125_CR12","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: Computer Vision and Pattern Recognition, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"2125_CR13","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: Keypoint triplets for object detection. In: International Conference on Computer Vision, pp. 6569\u20136578 (2019)","DOI":"10.1109\/ICCV.2019.00667"},{"key":"2125_CR14","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"2125_CR15","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: the International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, pp. 315\u2013323 (2011)"},{"key":"2125_CR16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2125_CR17","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2125_CR18","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: Computer Vision and Pattern Recognition, pp. 1647\u20131655 (2017)","DOI":"10.1109\/CVPR.2017.179"},{"issue":"5","key":"2125_CR19","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1007\/s00371-018-1503-0","volume":"35","author":"L Kabbai","year":"2019","unstructured":"Kabbai, L., Abdellaoui, M., Douik, A.: Image classification by combining local and global features. Vis. Comput. 35(5), 679\u2013693 (2019)","journal-title":"Vis. Comput."},{"key":"2125_CR20","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. rep. (2009)"},{"key":"2125_CR21","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"2125_CR22","doi-asserted-by":"crossref","unstructured":"Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Neural Information Processing Systems, pp. 801\u2013808 (2007)","DOI":"10.7551\/mitpress\/7503.003.0105"},{"issue":"6","key":"2125_CR23","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/S0960-9822(03)00135-0","volume":"13","author":"P Lennie","year":"2003","unstructured":"Lennie, P.: The cost of cortical computation. Curr. Biol. 13(6), 493\u2013497 (2003)","journal-title":"Curr. Biol."},{"key":"2125_CR24","doi-asserted-by":"crossref","unstructured":"Li, X., Yang, Y., Zhao, Q., Shen, T., Lin, Z., Liu, H.: Spatial pyramid based graph reasoning for semantic segmentation. In: Computer Vision and Pattern Recognition, pp. 8947\u20138956 (2020)","DOI":"10.1109\/CVPR42600.2020.00897"},{"key":"2125_CR25","unstructured":"Liu, B., Wang, M., Foroosh, H., Tappen, M., Pensky, M.: Sparse convolutional neural networks. In: Computer Vision and Pattern Recognition, pp. 806\u2013814 (2015)"},{"key":"2125_CR26","unstructured":"Liu, C.T., Wu, Y.H., Lin, Y.S., Chien, S.Y.: A Kernel redundancy removing policy for convolutional neural network. CoRR arXiv:1705.10748 (2017)"},{"key":"2125_CR27","unstructured":"Mairal, J., Bach, F., Ponce, J., Sapiro, G. and Zisserman, A.: Supervised dictionary learning. arXiv preprint arXiv:0809.3083 (2008)"},{"key":"2125_CR28","unstructured":"Mishkin, D., Matas, J.: All you need is a good init. arXiv preprint arXiv:1511.06422 (2015)"},{"issue":"23","key":"2125_CR29","doi-asserted-by":"publisher","first-page":"3311","DOI":"10.1016\/S0042-6989(97)00169-7","volume":"37","author":"BA Olshausen","year":"1997","unstructured":"Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: A strategy employed by V1? Vis. Res. 37(23), 3311\u20133325 (1997)","journal-title":"Vis. Res."},{"key":"2125_CR30","unstructured":"Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)"},{"issue":"3","key":"2125_CR31","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s10489-019-01536-y","volume":"50","author":"S Shi","year":"2020","unstructured":"Shi, S., Ding, S., Zhang, Z., Jia, W.: Energy-based structural least squares MBSVM for classification. Appl. Intell. 50(3), 681\u2013697 (2020)","journal-title":"Appl. Intell."},{"key":"2125_CR32","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"2125_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2125_CR34","doi-asserted-by":"crossref","unstructured":"Wang, D., Hu, G., Lyu, C.: Frnet: an end-to-end feature refinement neural network for medical image segmentation. Vis. Comput. 1432\u20132315 (2020)","DOI":"10.1007\/s00371-020-01855-z"},{"key":"2125_CR35","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. arXiv preprint arXiv:1608.03665 (2016)"},{"key":"2125_CR36","doi-asserted-by":"crossref","unstructured":"Wu, B., Liu, Z., Yuan, Z., Sun, G., Wu, C.: Reducing overfitting in deep convolutional neural networks using redundancy regularizer. In: International Conference on Artificial Neural Networks, pp. 49\u201355 (2017)","DOI":"10.1007\/978-3-319-68612-7_6"},{"key":"2125_CR37","doi-asserted-by":"crossref","unstructured":"Xie, D., Xiong, J., Pu, S.: All you need is beyond a good init: Exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation. arXiv preprint arXiv:1703.01827 (2017)","DOI":"10.1109\/CVPR.2017.539"},{"key":"2125_CR38","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Gao, C., Yu, G., Shen, C., Sang, N.: Context prior for scene segmentation. In: Computer Vision and Pattern Recognition, pp. 12416\u201312425 (2020)","DOI":"10.1109\/CVPR42600.2020.01243"},{"key":"2125_CR39","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Xie, J., Chen, X., Wang, J.: Segfix: Model-agnostic boundary refinement for segmentation. In: European Conference on Computer Vision, pp. 489\u2013506 (2020)","DOI":"10.1007\/978-3-030-58610-2_29"},{"key":"2125_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ding, S., Zhang, N., Jia, W.: Adversarial training methods for boltzmann machines. IEEE Access, 8, 4594\u20134604 (2019)","DOI":"10.1109\/ACCESS.2019.2962758"},{"key":"2125_CR41","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ins.2019.12.062","volume":"516","author":"N Zhang","year":"2020","unstructured":"Zhang, N., Ding, S., Sun, T., Liao, H., Wang, L., Shi, Z.: Multi-view RBM with posterior consistency and domain adaptation. Inf. Sci. 516, 142\u2013157 (2020)","journal-title":"Inf. Sci."},{"key":"2125_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, C., Ni, B., Zhang, J., Zhao, Q., Zhang, W., Tian, Q.: Variational convolutional neural network pruning. In: Computer Vision and Pattern Recognition, pp. 2780\u20132789 (2019)","DOI":"10.1109\/CVPR.2019.00289"},{"key":"2125_CR43","doi-asserted-by":"crossref","unstructured":"Zhong, X., Gong, O., Huang, W., Li, L., Xia, H.: Squeeze-and-excitation wide residual networks in image classification. In: Conference on Image Processing, pp. 395\u2013399 (2019)","DOI":"10.1109\/ICIP.2019.8803000"},{"key":"2125_CR44","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. ArXiv Preprint arXiv:1904.07850 (2019)"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02125-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-021-02125-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02125-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T15:56:20Z","timestamp":1698940580000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-021-02125-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,23]]},"references-count":44,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["2125"],"URL":"https:\/\/doi.org\/10.1007\/s00371-021-02125-2","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2021,4,23]]},"assertion":[{"value":"26 March 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The all authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}