{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:25:56Z","timestamp":1762507556037,"version":"3.40.3"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030012571"},{"type":"electronic","value":"9783030012588"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-01258-8_12","type":"book-chapter","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T20:35:31Z","timestamp":1538771731000},"page":"192-208","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Sparsely Aggregated Convolutional Networks"],"prefix":"10.1007","author":[{"given":"Ligeng","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Ruizhi","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Maire","sequence":"additional","affiliation":[]},{"given":"Zhiwei","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Greg","family":"Mori","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Tan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,10,6]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. PAMI 39, 2481\u20132495 (2017)","journal-title":"PAMI"},{"doi-asserted-by":"crossref","unstructured":"Chang, B., Meng, L., Haber, E., Ruthotto, L., Begert, D., Holtham, E.: Reversible architectures for arbitrarily deep residual neural networks. In: AAAI (2018)","key":"12_CR2","DOI":"10.1609\/aaai.v32i1.11668"},{"unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv:1606.00915 (2016)","key":"12_CR3"},{"unstructured":"Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing neural networks with the hashing trick. In: ICML (2015)","key":"12_CR4"},{"unstructured":"Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., Feng, J.: Dual path networks. In: NIPS (2017)","key":"12_CR5"},{"doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)","key":"12_CR6","DOI":"10.1109\/CVPR.2009.5206848"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)","key":"12_CR7","DOI":"10.1109\/CVPR.2014.81"},{"unstructured":"Gomez, A.N., Ren, M., Urtasun, R., Grosse, R.B.: The reversible residual network: backpropagation without storing activations. In: NIPS (2017)","key":"12_CR8"},{"unstructured":"Gray, S., Radford, A., Kingma, D.P.: GPU kernels for block-sparse weights. Technical report, OpenAI (2017)","key":"12_CR9"},{"unstructured":"Greff, K., Srivastava, R.K., Schmidhuber, J.: Highway and residual networks learn unrolled iterative estimation. In: ICLR (2017)","key":"12_CR10"},{"unstructured":"Gross, S., Wilber, M.: Training and investigating residual nets (2016). https:\/\/github.com\/facebook\/fb.resnet.torch","key":"12_CR11"},{"unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and huffman coding. In: ICLR (2016)","key":"12_CR12"},{"doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbelaez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)","key":"12_CR13","DOI":"10.1109\/CVPR.2015.7298642"},{"doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)","key":"12_CR14","DOI":"10.1109\/ICCV.2017.322"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: ICCV (2015)","key":"12_CR15","DOI":"10.1109\/ICCV.2015.123"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","key":"12_CR16","DOI":"10.1109\/CVPR.2016.90"},{"key":"12_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"unstructured":"Hu, H., Dey, D., Giorno, A.D., Hebert, M., Bagnell, J.A.: Log-DenseNet: How to sparsify a DenseNet. arXiv:1711.00002 (2017)","key":"12_CR18"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, S., van der Maaten, L., Weinberger, K.Q.: CondenseNet: an efficient densenet using learned group convolutions. In: CVPR (2018)","key":"12_CR19","DOI":"10.1109\/CVPR.2018.00291"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)","key":"12_CR20","DOI":"10.1109\/CVPR.2017.243"},{"key":"12_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1007\/978-3-319-46493-0_39","volume-title":"Computer Vision \u2013 ECCV 2016","author":"G Huang","year":"2016","unstructured":"Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 646\u2013661. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_39"},{"unstructured":"Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $$<$$1MB model size. arXiv:1602.07360 (2016)","key":"12_CR22"},{"unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)","key":"12_CR23"},{"unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report, University of Toronto (2009)","key":"12_CR24"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)","key":"12_CR25"},{"unstructured":"Larsson, G., Maire, M., Shakhnarovich, G.: FractalNet: Ultra-deep neural networks without residuals. In: ICLR (2017)","key":"12_CR26"},{"unstructured":"Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: AISTATS (2015)","key":"12_CR27"},{"doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)","key":"12_CR28","DOI":"10.1109\/CVPR.2015.7298965"},{"unstructured":"Paszke, A., et al.: PyTorch: tensors and dynamic neural networks in python with strong GPU acceleration, May 2017","key":"12_CR29"},{"doi-asserted-by":"crossref","unstructured":"Prabhu, A., Varma, G., Namboodiri, A.M.: Deep expander networks: efficient deep networks from graph theory. arXiv:1711.08757 (2017)","key":"12_CR30","DOI":"10.1007\/978-3-030-01261-8_2"},{"key":"12_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)","key":"12_CR32"},{"unstructured":"Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. arXiv:1505.00387 (2015)","key":"12_CR33"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: AAAI (2017)","key":"12_CR34","DOI":"10.1609\/aaai.v31i1.11231"},{"doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: CVPR (2015)","key":"12_CR35","DOI":"10.1109\/CVPR.2015.7298594"},{"doi-asserted-by":"crossref","unstructured":"Wang, W., Li, X., Yang, J., Lu, T.: Mixed link networks. arXiv:1802.01808 (2018)","key":"12_CR36","DOI":"10.24963\/ijcai.2018\/391"},{"unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. In: NIPS (2016)","key":"12_CR37"},{"unstructured":"Xiao, L., Bahri, Y., Sohl-Dickstein, J., Schoenholz, S.S., Pennington, J.: Dynamical isometry and a mean field theory of CNNs: how to train 10,000-layer vanilla convolutional neural networks. In: ICML (2018)","key":"12_CR38"},{"doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: CVPR (2017)","key":"12_CR39","DOI":"10.1109\/CVPR.2017.634"},{"doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: BMVC (2016)","key":"12_CR40","DOI":"10.5244\/C.30.87"},{"doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)","key":"12_CR41","DOI":"10.1109\/CVPR.2017.660"},{"unstructured":"Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. arXiv:1611.01578 (2016)","key":"12_CR42"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01258-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:37:15Z","timestamp":1664930235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01258-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012571","9783030012588"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01258-8_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 October 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}