{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:42:07Z","timestamp":1771026127776,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,5,15]],"date-time":"2020-05-15T00:00:00Z","timestamp":1589500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,5,15]],"date-time":"2020-05-15T00:00:00Z","timestamp":1589500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002635","name":"Inha University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002635","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s10489-020-01720-5","type":"journal-article","created":{"date-parts":[[2020,5,15]],"date-time":"2020-05-15T10:03:06Z","timestamp":1589536986000},"page":"3239-3251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Effective node selection technique towards sparse learning"],"prefix":"10.1007","volume":"50","author":[{"given":"Bunyodbek","family":"Ibrokhimov","sequence":"first","affiliation":[]},{"given":"Cheonghwan","family":"Hur","sequence":"additional","affiliation":[]},{"given":"Sanggil","family":"Kang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,15]]},"reference":[{"key":"1720_CR1","doi-asserted-by":"crossref","unstructured":"R. Girshick, J. Donahue, T. Darrell, and J. Malik, \\Rich feature hierarchies for accurate object detection and semantic segmentation,\" in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580\u2013587","DOI":"10.1109\/CVPR.2014.81"},{"key":"1720_CR2","doi-asserted-by":"crossref","unstructured":"R. Girshick, \u201cFast r-cnn,\" in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"key":"1720_CR3","unstructured":"J.-J. Hwang and T.-L. Liu, \u201cPixel-wise deep learning for contour detection,\" arXiv preprint arXiv:1504.01989, 2015"},{"key":"1720_CR4","doi-asserted-by":"crossref","unstructured":"Johnson, J., Karpathy, A., & Fei-Fei, L. (2016). Densecap: fully convolutional localization networks for dense captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4565-4574)","DOI":"10.1109\/CVPR.2016.494"},{"key":"1720_CR5","doi-asserted-by":"crossref","unstructured":"Chen, W., Wilson, J., Tyree, S., Weinberger, K. Q., & Chen, Y. (2016). Compressing convolutional neural networks in the frequency domain. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1475-1484)","DOI":"10.1145\/2939672.2939839"},{"issue":"8","key":"1720_CR6","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","volume":"35","author":"C Farabet","year":"2012","unstructured":"Farabet C, Couprie C, Najman L, LeCun Y (2012) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915\u20131929","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1720_CR7","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431-3440)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1720_CR8","doi-asserted-by":"crossref","unstructured":"Bansal, A., Russell, B., & Gupta, A. (2016). Marr revisited: 2d-3d alignment via surface normal prediction. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5965-5974)","DOI":"10.1109\/CVPR.2016.642"},{"key":"1720_CR9","doi-asserted-by":"crossref","unstructured":"Eigen, D., & Fergus, R. (2015). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In Proceedings of the IEEE international conference on computer vision (pp. 2650-2658)","DOI":"10.1109\/ICCV.2015.304"},{"key":"1720_CR10","doi-asserted-by":"crossref","unstructured":"Wang, X., Fouhey, D., & Gupta, A. (2015). Designing deep networks for surface normal estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 539-547)","DOI":"10.1109\/CVPR.2015.7298652"},{"key":"1720_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1720_CR12","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99)"},{"key":"1720_CR13","unstructured":"Sermanet, P., Eigen, D., Mathieu, M., Zhang, X., Fergus, R., & Lecun, Y. (2013). OverFeat detection using deep learning. In International Conference on Learning Representations (ICLR) (Vol. 16)"},{"issue":"12","key":"1720_CR14","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.3390\/app8122512","volume":"8","author":"G Boukli Hacene","year":"2018","unstructured":"Boukli Hacene G, Gripon V, Farrugia N, Arzel M, Jezequel M (2018) Transfer incremental learning using data augmentation. Appl Sci 8(12):2512","journal-title":"Appl Sci"},{"key":"1720_CR15","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105)"},{"key":"1720_CR16","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"1720_CR17","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1\u20139)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1720_CR18","doi-asserted-by":"crossref","unstructured":"Han, K., Vedaldi, A., & Zisserman, A. (2019). Learning to discover novel visual categories via deep transfer clustering. In Proceedings of the IEEE International Conference on Computer Vision (pp. 8401-8409)","DOI":"10.1109\/ICCV.2019.00849"},{"key":"1720_CR19","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.vlsi.2018.11.001","volume":"67","author":"B Harris","year":"2019","unstructured":"Harris B, Bae I, Egger B (2019) Architectures and algorithms for on-device user customization of CNNs. Integration 67:121\u2013133","journal-title":"Integration"},{"key":"1720_CR20","doi-asserted-by":"crossref","unstructured":"Lawrence, N. D., & Platt, J. C. (2004). Learning to learn with the informative vector machine. In Proceedings of the twenty-first international conference on Machine learning (p. 65)","DOI":"10.1145\/1015330.1015382"},{"key":"1720_CR21","unstructured":"Bonilla, E. V., Chai, K. M., & Williams, C. (2008). Multi-task Gaussian process prediction. In Advances in neural information processing systems (pp. 153-160)"},{"key":"1720_CR22","unstructured":"Schwaighofer, A., Tresp, V., & Yu, K. (2005). Learning Gaussian process kernels via hierarchical Bayes. In Advances in neural information processing systems (pp. 1209-1216)"},{"key":"1720_CR23","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826)","DOI":"10.1109\/CVPR.2016.308"},{"key":"1720_CR24","unstructured":"Wang, Y. X., & Hebert, M. (2016). Learning from small sample sets by combining unsupervised meta-training with CNNs. In Advances in Neural Information Processing Systems (pp. 244-252)"},{"key":"1720_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., & Fu, Y. (2019). Large scale incremental learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 374-382)","DOI":"10.1109\/CVPR.2019.00046"},{"issue":"Nov","key":"1720_CR26","first-page":"1817","volume":"6","author":"RK Ando","year":"2005","unstructured":"Ando RK, Zhang T (2005) A framework for learning predictive structures from multiple tasks and unlabeled data. J Mach Learn Res 6(Nov):1817\u20131853","journal-title":"J Mach Learn Res"},{"key":"1720_CR27","doi-asserted-by":"crossref","unstructured":"Castro, F. M., Mar\u00edn-Jim\u00e9nez, M. J., Guil, N., Schmid, C., & Alahari, K. (2018). End-to-end incremental learning. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 233-248)","DOI":"10.1007\/978-3-030-01258-8_15"},{"issue":"3","key":"1720_CR28","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10994-007-5040-8","volume":"73","author":"A Argyriou","year":"2008","unstructured":"Argyriou A, Evgeniou T, Pontil M (2008) Convex multi-task feature learning. Mach Learn 73(3):243\u2013272","journal-title":"Mach Learn"},{"key":"1720_CR29","unstructured":"Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"1720_CR30","doi-asserted-by":"crossref","unstructured":"Wu, J., Leng, C., Wang, Y., Hu, Q., & Cheng, J. (2016). Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4820-4828)","DOI":"10.1109\/CVPR.2016.521"},{"issue":"1","key":"1720_CR31","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/MSP.2010.939038","volume":"28","author":"D Yu","year":"2010","unstructured":"Yu D, Deng L (2010) Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Process Mag 28(1):145\u2013154","journal-title":"IEEE Signal Process Mag"},{"key":"1720_CR32","unstructured":"Denil, M., Shakibi, B., Dinh, L., Ranzato, M. A., & De Freitas, N. (2013). Predicting parameters in deep learning. In Advances in neural information processing systems (pp. 2148-2156)"},{"issue":"10","key":"1720_CR33","doi-asserted-by":"publisher","first-page":"4730","DOI":"10.1109\/TNNLS.2017.2774288","volume":"29","author":"J Cheng","year":"2017","unstructured":"Cheng J, Wu J, Leng C, Wang Y, Hu Q (2017) Quantized CNN: a unified approach to accelerate and compress convolutional networks. IEEE Trans Neural Networks Learning Syst 29(10):4730\u20134743","journal-title":"IEEE Trans Neural Networks Learning Syst"},{"key":"1720_CR34","unstructured":"Hu, H., Peng, R., Tai, Y. W., & Tang, C. K. (2016). Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint arXiv:1607.03250"},{"issue":"6","key":"1720_CR35","doi-asserted-by":"publisher","first-page":"2950","DOI":"10.1007\/s11227-018-2684-z","volume":"75","author":"C Hur","year":"2019","unstructured":"Hur C, Kang S (2019) Entropy-based pruning method for convolutional neural networks. J Supercomput 75(6):2950\u20132963","journal-title":"J Supercomput"},{"key":"1720_CR36","unstructured":"Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both weights and connections for efficient neural network. In Advances in neural information processing systems (pp. 1135-1143)"},{"issue":"11","key":"1720_CR37","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":"1720_CR38","doi-asserted-by":"crossref","unstructured":"Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop (pp. 178-178). IEEE","DOI":"10.1109\/CVPR.2004.383"},{"key":"1720_CR39","unstructured":"Doersch, C., & Zisserman, A. (2019). Sim2real transfer learning for 3D human pose estimation: motion to the rescue. In Advances in Neural Information Processing Systems (pp. 12929-12941)"},{"key":"1720_CR40","doi-asserted-by":"crossref","unstructured":"Dawalatabad, N., Madikeri, S., Sekhar, C. C., & Murthy, H. A. (2019). Incremental transfer learning in two-pass information bottleneck based speaker Diarization system for meetings. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6291-6295). IEEE","DOI":"10.1109\/ICASSP.2019.8683114"},{"key":"1720_CR41","first-page":"5235","volume":"5","author":"J Deng","year":"2017","unstructured":"Deng J, Fr\u00fchholz S, Zhang Z, Schuller B (2017) Recognizing emotions from whispered speech based on acoustic feature transfer learning. IEEE Access 5:5235\u20135246","journal-title":"IEEE Access"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01720-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01720-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01720-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T23:29:49Z","timestamp":1621034989000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01720-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,15]]},"references-count":41,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["1720"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01720-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,15]]},"assertion":[{"value":"15 May 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}