{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:08:35Z","timestamp":1765357715990,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680655","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"3431-3439","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Reversing Structural Pattern Learning with Biologically Inspired Knowledge Distillation for Spiking Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9245-5544","authenticated-orcid":false,"given":"Qi","family":"Xu","sequence":"first","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0160-8950","authenticated-orcid":false,"given":"Yaxin","family":"Li","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6437-2087","authenticated-orcid":false,"given":"Xuanye","family":"Fang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3683-3779","authenticated-orcid":false,"given":"Jiangrong","family":"Shen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0609-0337","authenticated-orcid":false,"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-6181","authenticated-orcid":false,"given":"Gang","family":"Pan","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Building functional networks of spiking model neurons. Nature neuroscience","author":"Abbott Larry F","year":"2016","unstructured":"Larry F Abbott, Brian DePasquale, and Raoul-Martin Memmesheimer. 2016. Building functional networks of spiking model neurons. Nature neuroscience, Vol. 19, 3 (2016), 350--355."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. PMLR, 3701--3715","author":"Chen Yanqi","year":"2022","unstructured":"Yanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tiejun Huang, and Yonghong Tian. 2022. State transition of dendritic spines improves learning of sparse spiking neural networks. In International Conference on Machine Learning. PMLR, 3701--3715."},{"key":"e_1_3_2_1_3_1","volume-title":"Rethinking the performance comparison between SNNS and ANNS. Neural networks","author":"Deng Lei","year":"2020","unstructured":"Lei Deng, Yujie Wu, Xing Hu, Ling Liang, Yufei Ding, Guoqi Li, Guangshe Zhao, Peng Li, and Yuan Xie. 2020. Rethinking the performance comparison between SNNS and ANNS. Neural networks, Vol. 121 (2020), 294--307."},{"key":"e_1_3_2_1_4_1","volume-title":"Optimal conversion of conventional artificial neural networks to spiking neural networks. arXiv preprint arXiv:2103.00476","author":"Deng Shikuang","year":"2021","unstructured":"Shikuang Deng and Shi Gu. 2021. Optimal conversion of conventional artificial neural networks to spiking neural networks. arXiv preprint arXiv:2103.00476 (2021)."},{"key":"e_1_3_2_1_5_1","volume-title":"Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting. arXiv preprint arXiv:2202.11946","author":"Deng Shikuang","year":"2022","unstructured":"Shikuang Deng, Yuhang Li, Shanghang Zhang, and Shi Gu. 2022. Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting. arXiv preprint arXiv:2202.11946 (2022)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00266"},{"key":"e_1_3_2_1_7_1","volume-title":"Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks. In European Conference on Computer Vision. Springer, 52--68","author":"Guo Yufei","year":"2022","unstructured":"Yufei Guo, Liwen Zhang, Yuanpei Chen, Xinyi Tong, Xiaode Liu, YingLei Wang, Xuhui Huang, and Zhe Ma. 2022. Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks. In European Conference on Computer Vision. Springer, 52--68."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_23"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01357"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.08.001"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC18074.2021.9586266"},{"key":"e_1_3_2_1_12_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Jiang Tingting","year":"2023","unstructured":"Tingting Jiang, Qi Xu, Xuming Ran, Jiangrong Shen, Pan Lv, Qiang Zhang, and Gang Pan. 2023. Adaptive deep spiking neural network with global-local learning via balanced excitatory and inhibitory mechanism. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1905926116"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6787"},{"key":"e_1_3_2_1_15_1","volume-title":"Revisiting batch normalization for training low-latency deep spiking neural networks from scratch. Frontiers in neuroscience","author":"Kim Youngeun","year":"2021","unstructured":"Youngeun Kim and Priyadarshini Panda. 2021. Revisiting batch normalization for training low-latency deep spiking neural networks from scratch. Frontiers in neuroscience (2021), 1638."},{"key":"e_1_3_2_1_16_1","volume-title":"Distilling Spikes: Knowledge Distillation in Spiking Neural Networks. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 4536--4543","author":"Kushawaha Ravi Kumar","year":"2021","unstructured":"Ravi Kumar Kushawaha, Saurabh Kumar, Biplab Banerjee, and Rajbabu Velmurugan. 2021. Distilling Spikes: Knowledge Distillation in Spiking Neural Networks. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 4536--4543."},{"key":"e_1_3_2_1_17_1","volume-title":"Energy-efficient Knowledge Distillation for Spiking Neural Networks. arXiv preprint arXiv:2106.07172","author":"Lee Dongjin","year":"2021","unstructured":"Dongjin Lee, Seongsik Park, Jongwan Kim, Wuhyeong Doh, and Sungroh Yoon. 2021. Energy-efficient Knowledge Distillation for Spiking Neural Networks. arXiv preprint arXiv:2106.07172 (2021)."},{"key":"e_1_3_2_1_18_1","volume-title":"Exploring knowledge distillation of deep neural networks for efficient hardware solutions","author":"Haitong Li.","year":"2018","unstructured":"Haitong Li. 2018. Exploring knowledge distillation of deep neural networks for efficient hardware solutions. University Of Stanford: CS230 course report (2018)."},{"key":"e_1_3_2_1_19_1","series-title":"Journal of Physics: Conference Series","volume-title":"Federal SNN distillation: A low-communication-cost federated learning framework for spiking neural networks","author":"Liu Zhetong","year":"2078","unstructured":"Zhetong Liu, Qiugang Zhan, Xiurui Xie, Bingchao Wang, and Guisong Liu. 2022. Federal SNN distillation: A low-communication-cost federated learning framework for spiking neural networks. In Journal of Physics: Conference Series, Vol. 2216. IOP Publishing, 012078."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2931595"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316781.3317822"},{"key":"e_1_3_2_1_22_1","volume-title":"Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. arXiv preprint arXiv:2005.01807","author":"Rathi Nitin","year":"2020","unstructured":"Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy. 2020. Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. arXiv preprint arXiv:2005.01807 (2020)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci11010052"},{"key":"e_1_3_2_1_24_1","volume-title":"Slayer: Spike layer error reassignment in time. Advances in neural information processing systems","author":"Shrestha Sumit B","year":"2018","unstructured":"Sumit B Shrestha and Garrick Orchard. 2018. Slayer: Spike layer error reassignment in time. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS48895.2020.9073948"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/COOLCHIPS52128.2021.9410323"},{"key":"e_1_3_2_1_27_1","volume-title":"Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in neuroscience","author":"Wu Yujie","year":"2018","unstructured":"Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi. 2018. Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in neuroscience, Vol. 12 (2018), 331."},{"key":"e_1_3_2_1_28_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Xu Qi","year":"2024","unstructured":"Qi Xu, Yuyuan Gao, Jiangrong Shen, Yaxin Li, Xuming Ran, Huajin Tang, and Gang Pan. 2024. Enhancing adaptive history reserving by spiking convolutional block attention module in recurrent neural networks. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_29_1","volume-title":"Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation. arXiv preprint arXiv:2304.05627","author":"Xu Qi","year":"2023","unstructured":"Qi Xu, Yaxin Li, Jiangrong Shen, Jian K Liu, Huajin Tang, and Gang Pan. 2023. Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation. arXiv preprint arXiv:2304.05627 (2023)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3232106"},{"key":"e_1_3_2_1_31_1","volume-title":"Training Spiking Neural Networks with Local Tandem Learning. arXiv preprint arXiv:2210.04532","author":"Yang Qu","year":"2022","unstructured":"Qu Yang, Jibin Wu, Malu Zhang, Yansong Chua, Xinchao Wang, and Haizhou Li. 2022. Training Spiking Neural Networks with Local Tandem Learning. arXiv preprint arXiv:2210.04532 (2022)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00396"},{"key":"e_1_3_2_1_33_1","volume-title":"The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural computation","author":"Zenke Friedemann","year":"2021","unstructured":"Friedemann Zenke and Tim P Vogels. 2021. The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural computation, Vol. 33, 4 (2021), 899--925."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3110991"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17320"}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Melbourne VIC Australia","acronym":"MM '24"},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680655","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680655","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:57Z","timestamp":1750295877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":35,"alternative-id":["10.1145\/3664647.3680655","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680655","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}