{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T11:45:25Z","timestamp":1762429525800,"version":"3.40.3"},"publisher-location":"Cham","reference-count":71,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031198021"},{"type":"electronic","value":"9783031198038"}],"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\/978-3-031-19803-8_17","type":"book-chapter","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T13:05:16Z","timestamp":1666443916000},"page":"278-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Interpretations Steered Network Pruning via\u00a0Amortized Inferred Saliency Maps"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9324-6135","authenticated-orcid":false,"given":"Alireza","family":"Ganjdanesh","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9699-1790","authenticated-orcid":false,"given":"Shangqian","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3483-8333","authenticated-orcid":false,"given":"Heng","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018)","key":"17_CR1"},{"doi-asserted-by":"crossref","unstructured":"Alqahtani, A., Xie, X., Jones, M.W., Essa, E.: Pruning CNN filters via quantifying the importance of deep visual representations. Comput. Vis. Image Underst. 208, 103220 (2021)","key":"17_CR2","DOI":"10.1016\/j.cviu.2021.103220"},{"doi-asserted-by":"crossref","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One 10(7), e0130140 (2015)","key":"17_CR3","DOI":"10.1371\/journal.pone.0130140"},{"unstructured":"Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)","key":"17_CR4"},{"unstructured":"Bronstein, M.M., Bruna, J., Cohen, T., Velickovic, P.: Geometric deep learning: grids, groups, graphs, geodesics, and gauges. CoRR abs\/2104.13478 (2021). https:\/\/arxiv.org\/abs\/2104.13478","key":"17_CR5"},{"unstructured":"Chen, J., Song, L., Wainwright, M., Jordan, M.: Learning to explain: an information-theoretic perspective on model interpretation. In: International Conference on Machine Learning, pp. 883\u2013892. PMLR (2018)","key":"17_CR6"},{"unstructured":"Chen, W., Wilson, J., Tyree, S., Weinberger, K., Chen, Y.: Compressing neural networks with the hashing trick. In: International Conference on Machine Learning, pp. 2285\u20132294 (2015)","key":"17_CR7"},{"doi-asserted-by":"crossref","unstructured":"Chin, T.W., Ding, R., Zhang, C., Marculescu, D.: Towards efficient model compression via learned global ranking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1518\u20131528 (2020)","key":"17_CR8","DOI":"10.1109\/CVPR42600.2020.00159"},{"unstructured":"Dabkowski, P., Gal, Y.: Real time image saliency for black box classifiers. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017, pp. 6967\u20136976, Long Beach, CA, USA (2017)","key":"17_CR9"},{"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: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","key":"17_CR10","DOI":"10.1109\/CVPR.2009.5206848"},{"doi-asserted-by":"crossref","unstructured":"Gao, S., Huang, F., Pei, J., Huang, H.: Discrete model compression with resource constraint for deep neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1899\u20131908 (2020)","key":"17_CR11","DOI":"10.1109\/CVPR42600.2020.00197"},{"doi-asserted-by":"crossref","unstructured":"Gao, S., Huang, F., Zhang, Y., Huang, H.: Disentangled differentiable network pruning. In: Proceedings of the European Conference on Computer Vision (ECCV) (2022)","key":"17_CR12","DOI":"10.1007\/978-3-031-20083-0_20"},{"doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","key":"17_CR13","DOI":"10.1109\/ICCV.2015.169"},{"unstructured":"Grathwohl, W., Wang, K., Jacobsen, J., Duvenaud, D., Norouzi, M., Swersky, K.: Your classifier is secretly an energy based model and you should treat it like one. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26\u201330 April 2020. OpenReview.net (2020). https:\/\/openreview.net\/forum?id=Hkxzx0NtDB","key":"17_CR14"},{"unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. Adv. Neural Inf. Process. Syst. 1135\u20131143 (2015)","key":"17_CR15"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","key":"17_CR16","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., Yang, Y.: Learning filter pruning criteria for deep convolutional neural networks acceleration. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2009\u20132018 (2020)","key":"17_CR17","DOI":"10.1109\/CVPR42600.2020.00208"},{"doi-asserted-by":"crossref","unstructured":"He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y.: Soft filter pruning for accelerating deep convolutional neural networks. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 2234\u20132240 (2018)","key":"17_CR18","DOI":"10.24963\/ijcai.2018\/309"},{"doi-asserted-by":"crossref","unstructured":"He, Y., Liu, P., Wang, Z., Hu, Z., Yang, Y.: Filter pruning via geometric median for deep convolutional neural networks acceleration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4340\u20134349 (2019)","key":"17_CR19","DOI":"10.1109\/CVPR.2019.00447"},{"doi-asserted-by":"crossref","unstructured":"He, Y., Lin, J., Liu, Z., Wang, H., Li, L.J., Han, S.: Amc: automl for model compression and acceleration on mobile devices. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 784\u2013800 (2018)","key":"17_CR20","DOI":"10.1007\/978-3-030-01234-2_48"},{"unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)","key":"17_CR21"},{"unstructured":"Hooker, S., Erhan, D., Kindermans, P.J., Kim, B.: A benchmark for interpretability methods in deep neural networks. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)","key":"17_CR22"},{"doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","key":"17_CR23","DOI":"10.1109\/CVPR.2017.243"},{"unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 448\u2013456. PMLR, Lille, France (07\u201309 Jul 2015). https:\/\/proceedings.mlr.press\/v37\/ioffe15.html","key":"17_CR24"},{"unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017). https:\/\/openreview.net\/forum?id=rkE3y85ee","key":"17_CR25"},{"unstructured":"Jethani, N., Sudarshan, M., Aphinyanaphongs, Y., Ranganath, R.: Have we learned to explain?: how interpretability methods can learn to encode predictions in their interpretations. In: International Conference on Artificial Intelligence and Statistics, pp. 1459\u20131467. PMLR (2021)","key":"17_CR26"},{"key":"17_CR27","first-page":"722","volume":"720","author":"JM Joyce","year":"2011","unstructured":"Joyce, J.M.: Kullback-Leibler divergence. Int. Encycl. Stat. Sci. 720, 722 (2011)","journal-title":"Int. Encycl. Stat. Sci."},{"unstructured":"Kang, M., Han, B.: Operation-aware soft channel pruning using differentiable masks. In: International Conference on Machine Learning (2020)","key":"17_CR28"},{"unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)","key":"17_CR29"},{"unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. ICLR (2017)","key":"17_CR30"},{"doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Towards compact cnns via collaborative compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6438\u20136447 (2021)","key":"17_CR31","DOI":"10.1109\/CVPR46437.2021.00637"},{"key":"17_CR32","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, J., Wang, L., Shi, S., Zhang, X.: Pruning and quantization for deep neural network acceleration: a survey. Neurocomputing 461, 370\u2013403 (2021)","journal-title":"Neurocomputing"},{"unstructured":"Liebenwein, L., Baykal, C., Lang, H., Feldman, D., Rus, D.: Provable filter pruning for efficient neural networks. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=BJxkOlSYDH","key":"17_CR33"},{"doi-asserted-by":"crossref","unstructured":"Lin, M., Ji, R., Wang, Y., Zhang, Y., Zhang, B., Tian, Y., Shao, L.: Hrank: filter pruning using high-rank feature map. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","key":"17_CR34","DOI":"10.1109\/CVPR42600.2020.00160"},{"doi-asserted-by":"crossref","unstructured":"Lin, M., Ji, R., Zhang, Y., Zhang, B., Wu, Y., Tian, Y.: Channel pruning via automatic structure search. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 673\u2013679 (2020)","key":"17_CR35","DOI":"10.24963\/ijcai.2020\/94"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Metapruning: meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3296\u20133305 (2019)","key":"17_CR36","DOI":"10.1109\/ICCV.2019.00339"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: ICCV (2017)","key":"17_CR37","DOI":"10.1109\/ICCV.2017.298"},{"doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","key":"17_CR38","DOI":"10.1109\/CVPR.2015.7298965"},{"unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768\u20134777 (2017)","key":"17_CR39"},{"unstructured":"Maddison, C.J., Mnih, A., Teh, Y.W.: The concrete distribution: a continuous relaxation of discrete random variables. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017). https:\/\/openreview.net\/forum?id=S1jE5L5gl","key":"17_CR40"},{"doi-asserted-by":"crossref","unstructured":"Molchanov, P., Mallya, A., Tyree, S., Frosio, I., Kautz, J.: Importance estimation for neural network pruning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11264\u201311272 (2019)","key":"17_CR41","DOI":"10.1109\/CVPR.2019.01152"},{"unstructured":"Peng, H., Wu, J., Chen, S., Huang, J.: Collaborative channel pruning for deep networks. In: International Conference on Machine Learning, pp. 5113\u20135122 (2019)","key":"17_CR42"},{"key":"17_CR43","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: ImageNet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 525\u2013542. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779\u2013788 (2016)","key":"17_CR44","DOI":"10.1109\/CVPR.2016.91"},{"unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)","key":"17_CR45"},{"doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201c Why should i trust you?\u201d explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","key":"17_CR46","DOI":"10.1145\/2939672.2939778"},{"key":"17_CR47","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":"Sabih, M., Hannig, F., Teich, J.: Utilizing explainable AI for quantization and pruning of deep neural networks. arXiv preprint arXiv:2008.09072 (2020)","key":"17_CR48"},{"doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","key":"17_CR49","DOI":"10.1109\/CVPR.2018.00474"},{"doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","key":"17_CR50","DOI":"10.1109\/ICCV.2017.74"},{"unstructured":"Shah, H., Jain, P., Netrapalli, P.: Do input gradients highlight discriminative features? Adv. Neural Inf. Process. Syst. 34 (2021)","key":"17_CR51"},{"unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: International Conference on Machine Learning, pp. 3145\u20133153. PMLR (2017)","key":"17_CR52"},{"unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: Workshop at International Conference on Learning Representations. Citeseer (2014)","key":"17_CR53"},{"unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568\u2013576 (2014)","key":"17_CR54"},{"unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015). https:\/\/arxiv.org\/abs\/1409.1556","key":"17_CR55"},{"unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F., Wattenberg, M.: Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)","key":"17_CR56"},{"unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Workshop Track Proceedings (2015). https:\/\/arxiv.org\/abs\/1412.6806","key":"17_CR57"},{"unstructured":"Srinivas, S., Fleuret, F.: Rethinking the role of gradient-based attribution methods for model interpretability. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021). https:\/\/openreview.net\/forum?id=dYeAHXnpWJ4","key":"17_CR58"},{"key":"17_CR59","first-page":"24604","volume":"34","author":"Y Sui","year":"2021","unstructured":"Sui, Y., Yin, M., Xie, Y., Phan, H., Aliari Zonouz, S., Yuan, B.: Chip: channel independence-based pruning for compact neural networks. Adv. Neural Inf. Process. Syst. 34, 24604\u201324616 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. In: Advances in Neural Information Processing Systems, pp. 2074\u20132082 (2016)","key":"17_CR60"},{"doi-asserted-by":"crossref","unstructured":"Yao, K., Cao, F., Leung, Y., Liang, J.: Deep neural network compression through interpretability-based filter pruning. Pattern Recogn. 119, 108056 (2021)","key":"17_CR61","DOI":"10.1016\/j.patcog.2021.108056"},{"unstructured":"Ye, M., Gong, C., Nie, L., Zhou, D., Klivans, A., Liu, Q.: Good subnetworks provably exist: pruning via greedy forward selection. In: International Conference on Machine Learning (2020)","key":"17_CR62"},{"doi-asserted-by":"crossref","unstructured":"Yeom, S.K., et al.: Pruning by explaining: a novel criterion for deep neural network pruning. Pattern Recogn. 115, 107899 (2021)","key":"17_CR63","DOI":"10.1016\/j.patcog.2021.107899"},{"unstructured":"Yoon, J., Jordon, J., van der Schaar, M.: Invase: instance-wise variable selection using neural networks. In: International Conference on Learning Representations (2018)","key":"17_CR64"},{"key":"17_CR65","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Gao, S., Huang, H.: Exploration and estimation for model compression. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 487\u2013496 (2021)","key":"17_CR66","DOI":"10.1109\/ICCV48922.2021.00054"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Gao, S., Huang, H.: Recover fair deep classification models via altering pre-trained structure. In: Proceedings of the European Conference on Computer Vision (ECCV) (2022)","key":"17_CR67","DOI":"10.1007\/978-3-031-19778-9_28"},{"doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","key":"17_CR68","DOI":"10.1109\/CVPR.2016.319"},{"issue":"10","key":"17_CR69","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1038\/nmeth.3547","volume":"12","author":"J Zhou","year":"2015","unstructured":"Zhou, J., Troyanskaya, O.G.: Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12(10), 931\u2013934 (2015)","journal-title":"Nat. Methods"},{"unstructured":"Zhuang, Z., et al.: Discrimination-aware channel pruning for deep neural networks. In: Advances in Neural Information Processing Systems, pp. 875\u2013886 (2018)","key":"17_CR70"},{"unstructured":"Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: prediction difference analysis. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017). https:\/\/openreview.net\/forum?id=BJ5UeU9xx","key":"17_CR71"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19803-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T14:07:18Z","timestamp":1682604438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19803-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198021","9783031198038"],"references-count":71,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19803-8_17","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":"23 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 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":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}