{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:17:56Z","timestamp":1743121076794,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031147135"},{"type":"electronic","value":"9783031147142"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-14714-2_32","type":"book-chapter","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T21:03:13Z","timestamp":1660424593000},"page":"459-473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Neural Network Pruning by\u00a0Cooperative Coevolution"],"prefix":"10.1007","author":[{"given":"Jia-Liang","family":"Wu","sequence":"first","affiliation":[]},{"given":"Haopu","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Wenjing","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Qian","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195099713.001.0001","volume-title":"Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming","author":"T B\u00e4ck","year":"1996","unstructured":"B\u00e4ck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming. Genetic Algorithms. Oxford University Press, Oxford, UK (1996)"},{"issue":"1","key":"32_CR2","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1631\/FITEE.1700789","volume":"19","author":"J Cheng","year":"2018","unstructured":"Cheng, J., Wang, P., Li, G., Hu, Q., Lu, H.: Recent advances in efficient computation of deep convolutional neural networks. Front. Inf. Technol. Electron. Eng. 19(1), 64\u201377 (2018). https:\/\/doi.org\/10.1631\/FITEE.1700789","journal-title":"Front. Inf. Technol. Electron. Eng."},{"issue":"2","key":"32_CR3","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Duan, R., Ma, X., Wang, Y., Bailey, J., Qin, A.K., Yang, Y.: Adversarial camouflage: hiding physical-world attacks with natural styles. In: Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, pp. 997\u20131005 (2020)","DOI":"10.1109\/CVPR42600.2020.00108"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Eykholt, K., et al.: Robust physical-world attacks on deep learning visual classification. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, pp. 1625\u20131634 (2018)","DOI":"10.1109\/CVPR.2018.00175"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, pp. 580\u2013587 (2014)","DOI":"10.1109\/CVPR.2014.81"},{"key":"32_CR7","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA (2015)"},{"key":"32_CR8","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. In: Proceedings of the 4th International Conference on Learning Representations (ICLR), San Juan, Puerto Rico (2016)"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"32_CR10","unstructured":"Huang, H., Wang, Y., Erfani, S., Gu, Q., Bailey, J., Ma, X.: Exploring architectural ingredients of adversarially robust deep neural networks. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34, New Orleans, LA, pp. 5545\u20135559 (2021)"},{"key":"32_CR11","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto, Toronto, Canada (2009)"},{"key":"32_CR12","unstructured":"Kundu, S., Nazemi, M., Beerel, P.A., Pedram, M.: DNR: a tunable robust pruning framework through dynamic network rewiring of DNNs. In: Proceedings of the 26th Asia and South Pacific Design Automation Conference (ASPDAC), Tokyo, Japan, pp. 344\u2013350 (2021)"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Li, G., Qian, C., Jiang, C., Lu, X., Tang, K.: Optimization based layer-wise magnitude-based pruning for DNN compression. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, pp. 2383\u20132389 (2018)","DOI":"10.24963\/ijcai.2018\/330"},{"key":"32_CR14","unstructured":"Li, G., Yang, P., Qian, C., Hong, R., Tang., K.: Magnitude-based pruning for recurrent neural networks. IEEE Trans. Neural Networks Learn. Syst. (in press)"},{"key":"32_CR15","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: Proceedings of the 5th International Conference on Learning Representations (ICLR), Toulon, France (2017)"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Lin, M., et al.: HRank: filter pruning using high-rank feature map. In: Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Los Alamitos, CA, pp. 1526\u20131535 (2020)","DOI":"10.1109\/CVPR42600.2020.00160"},{"key":"32_CR17","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: Proceedings of the 5th International Conference on Learning Representations (ICLR), Toulon, France (2017)"},{"issue":"107461","key":"32_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107461","volume":"107","author":"J Luo","year":"2020","unstructured":"Luo, J., Wu, J.: Autopruner: an end-to-end trainable filter pruning method for efficient deep model inference. Pattern Recogn. 107(107461), 107461 (2020)","journal-title":"Pattern Recogn."},{"key":"32_CR19","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: Proceedings of the 6th International Conference on Learning Representations (ICLR), Vancouver, Canada (2018)"},{"key":"32_CR20","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: Advances in Neural Information Processing Systems, Workshop (NeurIPS) (2011)"},{"key":"32_CR21","unstructured":"Sehwag, V., Wang, S., Mittal, P., Jana, S.: Towards compact and robust deep neural networks. CoRR p. abs\/1906.06110 (2019)"},{"key":"32_CR22","unstructured":"Sehwag, V., Wang, S., Mittal, P., Jana, S.: HYDRA: pruning adversarially robust neural networks. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 33, Vancouver, Canada, pp. 19655\u201319666 (2020)"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Shang, H., Wu, J.L., Hong, W., Qian, C.: Neural network pruning by cooperative coevolution. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), Vienna, Austria (2022)","DOI":"10.24963\/ijcai.2022\/667"},{"key":"32_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA (2015)"},{"key":"32_CR25","unstructured":"Wu, B., Chen, J., Cai, D., He, X., Gu, Q.: Do wider neural networks really help adversarial robustness? In: Advances in Neural Information Processing Systems (NeurIPS), vol. 34, New Orleans, LA New Orleans, LA, pp. 7054\u20137067 (2021)"},{"issue":"9","key":"32_CR26","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1109\/5.784219","volume":"87","author":"X Yao","year":"1999","unstructured":"Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423\u20131447 (1999)","journal-title":"Proc. IEEE"},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Ye, S., et al.: Adversarial robustness vs. model compression, or both? In: Proceedings of the 2019 IEEE International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp. 111\u2013120 (2019)","DOI":"10.1109\/ICCV.2019.00020"},{"key":"32_CR28","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Proceedings of the 2016 British Machine Vision Conference (BMVC), York, UK (2016)","DOI":"10.5244\/C.30.87"},{"key":"32_CR29","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E.P., Ghaoui, L.E., Jordan, M.I.: Theoretically principled trade-off between robustness and accuracy. In: Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach CA, pp. 7472\u20137482 (2019)"},{"key":"32_CR30","unstructured":"Zhou, A., Yao, A., Guo, Y., Xu, L., Chen, Y.: Incremental network quantization: Towards lossless CNNs with low-precision weights. In: Proceedings of the 5th International Conference on Learning Representations (ICLR), Toulon, France (2017)"},{"key":"32_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-5956-9","volume-title":"Evolutionary Learning: Advances in Theories and Algorithms","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Yu, Y., Qian, C.: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore (2019)"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14714-2_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:45:45Z","timestamp":1710261945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14714-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031147135","9783031147142"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14714-2_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dortmund","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2022.cs.tu-dortmund.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"185","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.75","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.11","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}