{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:56:19Z","timestamp":1781193379903,"version":"3.54.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T00:00:00Z","timestamp":1556496000000},"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":["J Sign Process Syst"],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s11265-019-01450-z","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T11:03:07Z","timestamp":1556535787000},"page":"1063-1073","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Budget Restricted Incremental Learning with Pre-Trained Convolutional Neural Networks and Binary Associative Memories"],"prefix":"10.1007","volume":"91","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6855-5825","authenticated-orcid":false,"given":"Ghouthi","family":"Boukli Hacene","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Gripon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Farrugia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthieu","family":"Arzel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michel","family":"Jezequel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,4,29]]},"reference":[{"key":"1450_CR1","doi-asserted-by":"crossref","unstructured":"Wu, J., Leng, C., Wang, Y., et al. (2016). Quantized convolutional neural networks for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4820\u20134828).","DOI":"10.1109\/CVPR.2016.521"},{"key":"1450_CR2","unstructured":"Gong, Y., Liu, L., Yang, M., et al. (2014). Compressing deep convolutional networks using vector quantization. arXiv:\n                    1412.6115\n                    \n                  ."},{"key":"1450_CR3","unstructured":"Courbariaux, M., Bengio, Y., David, J.-P. (2015). Binaryconnect: Training deep neural networks with binary weights during propagations. In Advances in Neural Information Processing Systems (pp. 3123\u20133131)."},{"key":"1450_CR4","unstructured":"Souli\u00e9, G., Gripon, V., Robert, M. (2016). Compression of deep neural networks on the fly. In International Conference on Artificial Neural Networks (pp. 153\u2013160). Cham: Springer."},{"key":"1450_CR5","unstructured":"Suda, N., Chandra, V., Dasika, G., et al. (2016). Throughput-optimized openCL-based FPGA accelerator for large-scale convolutional neural networks. In Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays (pp. 16\u201325): ACM."},{"key":"1450_CR6","unstructured":"Bo, G.M., Caviglia, D.D., Valle, M. (2000). An on-chip learning neural network. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000. IJCNN 2000 (pp. 66\u201371): IEEE."},{"key":"1450_CR7","unstructured":"Choi, Y., EL-Khamy, M., Lee, J. (2016). Towards the limit of network quantization. arXiv:\n                    1612.01543\n                    \n                  ."},{"issue":"6","key":"1450_CR8","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TKDE.2016.2526675","volume":"28","author":"Y Sun","year":"2016","unstructured":"Sun, Y., Tang, K., Minku, L.L., et al. (2016). Online ensemble learning of data streams with gradually evolved classes. IEEE Trans. Knowl. Data Eng., 28(6), 1532\u20131545.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"1450_CR9","doi-asserted-by":"crossref","unstructured":"Syed, N.A., Huan, S., Kah, L., et al. (1999). Incremental learning with support vector machines.","DOI":"10.1145\/312129.312267"},{"key":"1450_CR10","unstructured":"Cauwenberghs, G., & Poggio, T. (2001). Incremental and decremental support vector machine learning. In Advances in Neural Information Processing Systems (pp. 409\u2013415)."},{"key":"1450_CR11","unstructured":"Lomonaco, V., & Maltoni, D. (2016). Comparing incremental learning strategies for convolutional neural networks. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 175\u2013184). Cham: Springer."},{"issue":"5","key":"1450_CR12","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.1007\/s00521-011-0793-1","volume":"22","author":"J Zheng","year":"2013","unstructured":"Zheng, J., Shen, F., Fan, H., et al. (2013). An online incremental learning support vector machine for large-scale data. Neural Comput. & Applic., 22(5), 1023\u20131035.","journal-title":"Neural Comput. & Applic."},{"key":"1450_CR13","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., et al. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818\u20132826).","DOI":"10.1109\/CVPR.2016.308"},{"key":"1450_CR14","unstructured":"Oquab, M., Bottou, L., Laptev, I., et al. (2014). Learning and transferring mid-level image representations using convolutional neural networks. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1717\u20131724): IEEE."},{"issue":"10","key":"1450_CR15","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., & Yang, Q. (2010). A survey on transfer learning. IEEE Trans. Knowl. Data Eng., 22(10), 1345\u20131359.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"1450_CR16","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1109\/TNN.2011.2146789","volume":"22","author":"V Gripon","year":"2011","unstructured":"Gripon, V., & Berrou, C. (2011). Sparse neural networks with large learning diversity. IEEE Trans. Neural Netw., 22(7), 1087\u20131096.","journal-title":"IEEE Trans. Neural Netw."},{"key":"1450_CR17","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\u20131105)."},{"issue":"1","key":"1450_CR18","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TPAMI.2010.57","volume":"33","author":"H Jegou","year":"2011","unstructured":"Jegou, H., Douze, M., Schmid, C. (2011). Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell., 33(1), 117\u2013128.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1450_CR19","unstructured":"Hong, S., You, T., Kwak, S., et al. (2015). Online tracking by learning discriminative saliency map with convolutional neural network. In International Conference on Machine Learning (pp. 597\u2013606)."},{"key":"1450_CR20","unstructured":"Goodfellow, I. J., Mirza, M., Xiao, D., et al. (2013). An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv:\n                    1312.6211\n                    \n                  ."},{"issue":"3","key":"1450_CR21","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., et al. (2015). Imagenet large scale visual recognition challenge. Int. J. Comput. Vis., 115(3), 211\u2013252.","journal-title":"Int. J. Comput. Vis."},{"key":"1450_CR22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., et al. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 580\u2013587).","DOI":"10.1109\/CVPR.2014.81"},{"key":"1450_CR23","volume-title":"Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines","author":"N Kasabov","year":"2013","unstructured":"Kasabov, N. (2013). Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines. Berlin: Springer Science & Business Media."},{"issue":"4","key":"1450_CR24","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French, R.M. (1999). Catastrophic forgetting in connectionist networks. Trends Cogn. Sci., 3(4), 128\u2013135.","journal-title":"Trends Cogn. Sci."},{"issue":"4","key":"1450_CR25","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1109\/5326.983933","volume":"31","author":"R Polikar","year":"2001","unstructured":"Polikar, R., Upda, L., Upda, S.S., et al. (2001). Learn++: an incremental learning algorithm for supervised neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 31(4), 497\u2013508.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"key":"1450_CR26","doi-asserted-by":"crossref","unstructured":"Polikar, R., Udpa, L., Udpa, S.S., et al. (2000). Learn++: an incremental learning algorithm for multilayer perceptron networks. In 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2000. ICASSP?00. Proceedings (pp. 3414\u20133417).","DOI":"10.1109\/ICASSP.2000.860134"},{"key":"1450_CR27","unstructured":"Qiu, J., Wang, J., Yao, S., et al. (2016). Going deeper with embedded fpga platform for convolutional neural network. In Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays (pp. 26\u201335): ACM."},{"key":"1450_CR28","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., et al. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <\u20090.5 MB model size. arXiv:\n                    1602.07360\n                    \n                  ."},{"key":"1450_CR29","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:\n                    1409.1556\n                    \n                  ."},{"issue":"6","key":"1450_CR30","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton, G., Deng, L., Yu, D., et al. (2012). Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag., 29(6), 82\u201397.","journal-title":"IEEE Signal Process. Mag."},{"key":"1450_CR31","unstructured":"Graham, B. (2014). Fractional max-pooling. arXiv:\n                    1412.6071\n                    \n                  ."},{"key":"1450_CR32","doi-asserted-by":"crossref","unstructured":"Pentina, A., Sharmanska, V., Lampert, C.H. (2015). Curriculum learning of multiple tasks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5492\u2013 5500).","DOI":"10.1109\/CVPR.2015.7299188"},{"key":"1450_CR33","unstructured":"Kuzborskij, I., Orabona, F., Caputo, B. (2013). From n to n + 1: Multiclass transfer incremental learning. In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3358\u20133365): IEEE."},{"key":"1450_CR34","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.-A., Kolesnikov, A., Lampert, C.H. (2017). iCaRL: Incremental classifier and representation learning. In Proc. CVPR.","DOI":"10.1109\/CVPR.2017.587"}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-019-01450-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11265-019-01450-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-019-01450-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,27]],"date-time":"2020-04-27T23:12:29Z","timestamp":1588029149000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11265-019-01450-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,29]]},"references-count":34,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["1450"],"URL":"https:\/\/doi.org\/10.1007\/s11265-019-01450-z","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"value":"1939-8018","type":"print"},{"value":"1939-8115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,29]]},"assertion":[{"value":"2 March 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 February 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}