{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:21:29Z","timestamp":1769725289857,"version":"3.49.0"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031781650","type":"print"},{"value":"9783031781667","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78166-7_30","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:34:25Z","timestamp":1733088865000},"page":"463-477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EncodeNet: A Framework for\u00a0Boosting DNN Accuracy with\u00a0Entropy-Driven Generalized Converting Autoencoder"],"prefix":"10.1007","author":[{"given":"Hasanul","family":"Mahmud","sequence":"first","affiliation":[]},{"given":"Palden","family":"Lama","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Desai","sequence":"additional","affiliation":[]},{"given":"Sushil K.","family":"Prasad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2017)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN (2017). https:\/\/api.semanticscholar.org\/CorpusID:54465873","DOI":"10.1109\/ICCV.2017.322"},{"key":"30_CR4","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137\u20131149 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR5","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6230\u20136239 (2016)","DOI":"10.1109\/CVPR.2017.660"},{"key":"30_CR6","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640\u2013651 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"30_CR7","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","volume":"461","author":"T Liang","year":"2021","unstructured":"Liang, T., Glossner, C.J., Wang, L., Shi, S.: Pruning and quantization for deep neural network acceleration: a survey. Neurocomputing 461, 370\u2013403 (2021)","journal-title":"Neurocomputing"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Luo, J.-H., Wu, J., Lin, W.: ThiNet: a filter level pruning method for deep neural network compression. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5068\u20135076 (2017)","DOI":"10.1109\/ICCV.2017.541"},{"key":"30_CR9","doi-asserted-by":"publisher","unstructured":"Aghli, N., Ribeiro, E.: Combining weight pruning and knowledge distillation for CNN compression. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2021). https:\/\/doi.org\/10.1109\/CVPRW53098.2021.00356","DOI":"10.1109\/CVPRW53098.2021.00356"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Yang, T.-J., Chen, Y.-h., Sze, V.: Designing energy-efficient convolutional neural networks using energy-aware pruning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6071\u20136079 (2016)","DOI":"10.1109\/CVPR.2017.643"},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Li, J., Gong, Y.: Low-rank plus diagonal adaptation for deep neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2016). https:\/\/doi.org\/10.1109\/ICASSP.2016.7472630","DOI":"10.1109\/ICASSP.2016.7472630"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2704\u20132713 (2017)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"30_CR13","unstructured":"Louizos, C., Ullrich, K., Welling, M.: Bayesian compression for deep learning. arXiv preprint arxiv: abs\/1705.08665 (2017)"},{"key":"30_CR14","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"30_CR15","doi-asserted-by":"crossref","unstructured":"Beyer, L., Zhai, X., Royer, A., Markeeva, L., Anil, R., Kolesnikov, A.: Knowledge distillation: a good teacher is patient and consistent. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.01065"},{"key":"30_CR16","unstructured":"Kim, J., Park, S., Kwak, N.: Paraphrasing complex network: network compression via factor transfer. arXiv preprint arxiv: abs\/1802.04977 (2018)"},{"key":"30_CR17","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: Hints for Thin Deep Nets (2015)"},{"key":"30_CR18","unstructured":"Park, J., Woo, S., Lee, J.-Y., Kweon, I.S.: Bam: Bottleneck attention module. arXiv preprint arXiv:1807.06514 (2018)"},{"key":"30_CR19","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"30_CR20","unstructured":"Liu, Y., Shao, Z., Hoffmann, N.: Global attention mechanism: retain information to enhance channel-spatial interactions. ArXiv preprint arxiv: abs\/2112.05561 (2021)"},{"key":"30_CR21","doi-asserted-by":"publisher","unstructured":"Mahmud, H., Kang, P., Desai, K., Lama, P., Prasad, S.K.: A converting autoencoder toward low-latency and energy-efficient DNN inference at the edge. In: 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 592\u2013599 (2024). https:\/\/doi.org\/10.1109\/IPDPSW63119.2024.00117","DOI":"10.1109\/IPDPSW63119.2024.00117"},{"key":"30_CR22","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational Knowledge Distillation (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"30_CR23","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"30_CR24","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., Bottou, L.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(12) (2010)"},{"issue":"1","key":"30_CR25","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s00287-016-1013-2","volume":"40","author":"C Wick","year":"2016","unstructured":"Wick, C.: Deep Learning. Informatik-Spektrum 40(1), 103\u2013107 (2016). https:\/\/doi.org\/10.1007\/s00287-016-1013-2","journal-title":"Informatik-Spektrum"},{"key":"30_CR26","doi-asserted-by":"crossref","unstructured":"Kingma, D.P., Welling, M., et al.: An introduction to variational autoencoders. Found. Trends\u00ae Mach. Learn. 12(4), 307\u2013392 (2019)","DOI":"10.1561\/2200000056"},{"key":"30_CR27","doi-asserted-by":"crossref","unstructured":"Mahmud, H., Kang, P., Desai, K., Lama, P., Prasad, S.: A converting autoencoder toward low-latency and energy-efficient DNN inference at the edge. ArXiv - Accepted for publication at the PAISE workshop at IPDPS (2024)","DOI":"10.1109\/IPDPSW63119.2024.00117"},{"key":"30_CR28","doi-asserted-by":"crossref","unstructured":"Cho, J.H., Hariharan, B.: On the efficacy of knowledge distillation. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00489"},{"key":"30_CR29","unstructured":"Mishra, A., Marr, D.: Apprentice: using knowledge distillation techniques to improve low-precision network accuracy. arXiv preprint arXiv:1711.05852 (2017)"},{"key":"30_CR30","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. CoRR arxiv preprint arxiv: abs\/1412.6550 (2014)"},{"key":"30_CR31","unstructured":"Liu, S., Du, J., Nan, K., Zhou, Z., Wang, A., Lin, Y.: Adadeep: a usage-driven, automated deep model compression framework for enabling ubiquitous intelligent mobiles. CoRR arxiv preprint arxiv: abs\/2006.04432 (2020)"},{"key":"30_CR32","doi-asserted-by":"publisher","unstructured":"Lee, S., Nirjon, S.: Subflow: A dynamic induced-subgraph strategy toward real-time DNN inference and training. In: IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) (2020). https:\/\/doi.org\/10.1109\/RTAS48715.2020.00-20","DOI":"10.1109\/RTAS48715.2020.00-20"},{"key":"30_CR33","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"30_CR34","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetv2: inverted residuals and linear bottlenecks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"30_CR35","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. ArXiv preprint arxiv: abs\/1505.04597 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"30_CR36","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR arxiv preprint arxiv: abs\/1409.1556 (2014)"},{"key":"30_CR37","doi-asserted-by":"crossref","unstructured":"Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"4","key":"30_CR38","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/BF02289263","volume":"18","author":"R Thorndike","year":"1953","unstructured":"Thorndike, R.: Who belongs in the family? Psychometrika 18(4), 267\u2013276 (1953)","journal-title":"Psychometrika"},{"key":"30_CR39","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: USENIX Symposium on Operating Systems Design and Implementation (2016). https:\/\/api.semanticscholar.org\/CorpusID:6287870"},{"key":"30_CR40","unstructured":"mdistiller. https:\/\/github.com\/megvii-research\/mdistiller.git"},{"key":"30_CR41","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. ArXiv preprint arxiv: abs\/1912.01703 (2019)"},{"key":"30_CR42","unstructured":"Krizhevsky, A., Nair, V., Hinton, G.: CIFAR-10 (Canadian institute for advanced research)"},{"key":"30_CR43","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report (2009)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78166-7_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T23:39:00Z","timestamp":1733096340000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78166-7_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031781650","9783031781667"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78166-7_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}