{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:31:38Z","timestamp":1750221098593,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T00:00:00Z","timestamp":1545091200000},"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":[[2018,12,18]]},"DOI":"10.1145\/3293353.3293383","type":"proceedings-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T22:07:32Z","timestamp":1588630052000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["HSD-CNN"],"prefix":"10.1145","author":[{"given":"K.","family":"SaiRam","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayanta","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Patra","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Partha Pratim","family":"Das","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Kharagpur"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,5,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Very deep convolutional networks for large-scale image recognition. CoRR, abs\/1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman . Very deep convolutional networks for large-scale image recognition. CoRR, abs\/1409.1556 , 2014 . Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs\/1409.1556, 2014."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.314"},{"key":"e_1_3_2_1_4_1","volume-title":"Tree-cnn: A deep convolutional neural network for lifelong learning. CoRR, abs\/1802.05800","author":"Roy Deboleena","year":"2018","unstructured":"Deboleena Roy , Priyadarshini Panda , and Kaushik Roy . Tree-cnn: A deep convolutional neural network for lifelong learning. CoRR, abs\/1802.05800 , 2018 . Deboleena Roy, Priyadarshini Panda, and Kaushik Roy. Tree-cnn: A deep convolutional neural network for lifelong learning. CoRR, abs\/1802.05800, 2018."},{"key":"e_1_3_2_1_5_1","volume-title":"B-CNN: branch convolutional neural network for hierarchical classification. CoRR, abs\/1709.09890","author":"Zhu Xinqi","year":"2017","unstructured":"Xinqi Zhu and Michael Bain . B-CNN: branch convolutional neural network for hierarchical classification. CoRR, abs\/1709.09890 , 2017 . Xinqi Zhu and Michael Bain. B-CNN: branch convolutional neural network for hierarchical classification. CoRR, abs\/1709.09890, 2017."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.58"},{"key":"e_1_3_2_1_7_1","first-page":"598","volume-title":"Advances in neural information processing systems 2","author":"Cun Yann Le","year":"1990","unstructured":"Yann Le Cun , John S. Denker , and Sara A. Solla . Advances in neural information processing systems 2 . chapter Optimal Brain Damage, pages 598 -- 605 . Morgan Kaufmann Publishers Inc ., San Francisco, CA, USA, 1990 . Yann Le Cun, John S. Denker, and Sara A. Solla. Advances in neural information processing systems 2. chapter Optimal Brain Damage, pages 598--605. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1990."},{"key":"e_1_3_2_1_8_1","first-page":"164","volume-title":"Advances in Neural Information Processing Systems 5, [NIPS Conference]","author":"Hassibi Babak","year":"1993","unstructured":"Babak Hassibi and David G. Stork . Second order derivatives for network pruning: Optimal brain surgeon . In Advances in Neural Information Processing Systems 5, [NIPS Conference] , pages 164 -- 171 , San Francisco, CA , USA, 1993 . Morgan Kaufmann Publishers Inc . Babak Hassibi and David G. Stork. Second order derivatives for network pruning: Optimal brain surgeon. In Advances in Neural Information Processing Systems 5, [NIPS Conference], pages 164--171, San Francisco, CA, USA, 1993. Morgan Kaufmann Publishers Inc."},{"key":"e_1_3_2_1_9_1","first-page":"1135","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems -","volume":"1","author":"Han Song","year":"2015","unstructured":"Song Han , Jeff Pool , John Tran , and William J. Dally . Learning both weights and connections for efficient neural networks . In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 , NIPS'15, pages 1135 -- 1143 , Cambridge, MA, USA , 2015 . MIT Press. Song Han, Jeff Pool, John Tran, and William J. Dally. Learning both weights and connections for efficient neural networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, NIPS'15, pages 1135--1143, Cambridge, MA, USA, 2015. MIT Press."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3005348"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2494536"},{"key":"e_1_3_2_1_12_1","first-page":"1269","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems -","volume":"1","author":"Denton Emily","year":"2014","unstructured":"Emily Denton , Wojciech Zaremba , Joan Bruna , Yann LeCun , and Rob Fergus . Exploiting linear structure within convolutional networks for efficient evaluation . In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1 , NIPS'14, pages 1269 -- 1277 , Cambridge, MA, USA , 2014 . MIT Press. Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus. Exploiting linear structure within convolutional networks for efficient evaluation. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1, NIPS'14, pages 1269--1277, Cambridge, MA, USA, 2014. MIT Press."},{"key":"e_1_3_2_1_13_1","volume-title":"British Machine Vision Conference, BMVC 2014","author":"Jaderberg Max","year":"2014","unstructured":"Max Jaderberg , Andrea Vedaldi , and Andrew Zisserman . Speeding up convolutional neural networks with low rank expansions. In Michel Fran\u00e7ois Valstar, Andrew P. French, and Tony P. Pridmore, editors , British Machine Vision Conference, BMVC 2014 , Nottingham, UK , September 1-5, 2014 . BMVA Press, 2014. Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman. Speeding up convolutional neural networks with low rank expansions. In Michel Fran\u00e7ois Valstar, Andrew P. French, and Tony P. Pridmore, editors, British Machine Vision Conference, BMVC 2014, Nottingham, UK, September 1-5, 2014. BMVA Press, 2014."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.205"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2502579"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.78"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00083"},{"key":"e_1_3_2_1_19_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs\/1704.04861","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard , Menglong Zhu , Bo Chen , Dmitry Kalenichenko , Weijun Wang , Tobias Weyand , Marco Andreetto , and Hartwig Adam . Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs\/1704.04861 , 2017 . Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs\/1704.04861, 2017."},{"key":"e_1_3_2_1_20_1","volume-title":"Squeezenet: Alexnet-level accuracy with 50x fewer parameters and &lt;1mb model size. CoRR, abs\/1602.07360","author":"Iandola Forrest N.","year":"2016","unstructured":"Forrest N. Iandola , Matthew W. Moskewicz , Khalid Ashraf , Song Han , William J. Dally , and Kurt Keutzer . Squeezenet: Alexnet-level accuracy with 50x fewer parameters and &lt;1mb model size. CoRR, abs\/1602.07360 , 2016 . Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and &lt;1mb model size. CoRR, abs\/1602.07360, 2016."},{"key":"e_1_3_2_1_21_1","volume-title":"Shufflenet: An extremely efficient convolutional neural network for mobile devices. CoRR, abs\/1707.01083","author":"Zhang Xiangyu","year":"2017","unstructured":"Xiangyu Zhang , Xinyu Zhou , Mengxiao Lin , and Jian Sun . Shufflenet: An extremely efficient convolutional neural network for mobile devices. CoRR, abs\/1707.01083 , 2017 . Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. Shufflenet: An extremely efficient convolutional neural network for mobile devices. CoRR, abs\/1707.01083, 2017."},{"key":"e_1_3_2_1_22_1","first-page":"2752","volume-title":"2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018","author":"Huang Gao","year":"2018","unstructured":"Gao Huang , Shichen Liu , Laurens van der Maaten, and Kilian Q. Weinberger. Condensenet: An efficient densenet using learned group convolutions . In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018 , Salt Lake City, USA , June 18-22, 2018 , pages 2752 -- 2761 , 2018. Gao Huang, Shichen Liu, Laurens van der Maaten, and Kilian Q. Weinberger. Condensenet: An efficient densenet using learned group convolutions. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, USA, June 18-22, 2018, pages 2752--2761, 2018."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_2_1_24_1","volume-title":"Distilling a neural network into a soft decision tree. CoRR, abs\/1711.09784","author":"Frosst Nicholas","year":"2017","unstructured":"Nicholas Frosst and Geoffrey E. Hinton . Distilling a neural network into a soft decision tree. CoRR, abs\/1711.09784 , 2017 . Nicholas Frosst and Geoffrey E. Hinton. Distilling a neural network into a soft decision tree. CoRR, abs\/1711.09784, 2017."},{"key":"e_1_3_2_1_25_1","volume-title":"Ying Nian Wu, and Song-Chun Zhu. Interpreting cnns via decision trees. CoRR, abs\/1802.00121","author":"Zhang Quanshi","year":"2018","unstructured":"Quanshi Zhang , Yu Yang , Ying Nian Wu, and Song-Chun Zhu. Interpreting cnns via decision trees. CoRR, abs\/1802.00121 , 2018 . Quanshi Zhang, Yu Yang, Ying Nian Wu, and Song-Chun Zhu. Interpreting cnns via decision trees. CoRR, abs\/1802.00121, 2018."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1963.10500845"},{"key":"e_1_3_2_1_27_1","volume-title":"Pytorch: Tensors and dynamic neural networks in python with strong GPU acceleration","author":"Paszke Adam","year":"2017","unstructured":"Adam Paszke , Sam Gross , Soumith Chintala , and Gregory Chanan . Pytorch: Tensors and dynamic neural networks in python with strong GPU acceleration , 2017 . Last accessed on Mar 07, 2018. Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan. Pytorch: Tensors and dynamic neural networks in python with strong GPU acceleration, 2017. Last accessed on Mar 07, 2018."},{"key":"e_1_3_2_1_28_1","volume-title":"An overview of gradient descent optimization algorithms. CoRR, abs\/1609.04747","author":"Ruder Sebastian","year":"2016","unstructured":"Sebastian Ruder . An overview of gradient descent optimization algorithms. CoRR, abs\/1609.04747 , 2016 . Sebastian Ruder. An overview of gradient descent optimization algorithms. CoRR, abs\/1609.04747, 2016."},{"key":"e_1_3_2_1_29_1","volume-title":"Learning Multiple Layers of Features from Tiny Images. Master's thesis","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky . Learning Multiple Layers of Features from Tiny Images. Master's thesis , 2009 . Last accessed on 10 Aug, 2018. Alex Krizhevsky. Learning Multiple Layers of Features from Tiny Images. Master's thesis, 2009. Last accessed on 10 Aug, 2018."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2005.09.012"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.327"}],"event":{"name":"ICVGIP 2018: 11th Indian Conference on Computer Vision, Graphics and Image Processing","acronym":"ICVGIP 2018","location":"Hyderabad India"},"container-title":["Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3293353.3293383","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3293353.3293383","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:58:08Z","timestamp":1750208288000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3293353.3293383"}},"subtitle":["Hierarchically self decomposing CNN architecture using class specific filter sensitivity analysis"],"short-title":[],"issued":{"date-parts":[[2018,12,18]]},"references-count":31,"alternative-id":["10.1145\/3293353.3293383","10.1145\/3293353"],"URL":"https:\/\/doi.org\/10.1145\/3293353.3293383","relation":{},"subject":[],"published":{"date-parts":[[2018,12,18]]},"assertion":[{"value":"2020-05-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}