{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T21:44:03Z","timestamp":1775079843176,"version":"3.50.1"},"reference-count":100,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["62022009"],"award-info":[{"award-number":["62022009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61872021"],"award-info":[{"award-number":["61872021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"beijing nova program of science and technology","award":["Z191100001119050"],"award-info":[{"award-number":["Z191100001119050"]}]},{"name":"state key lab of software development environment","award":["SKLSDE-2020ZX-06"],"award-info":[{"award-number":["SKLSDE-2020ZX-06"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s11263-022-01687-5","type":"journal-article","created":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T07:02:33Z","timestamp":1664694153000},"page":"26-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Distribution-Sensitive Information Retention for Accurate Binary Neural Network"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7391-7539","authenticated-orcid":false,"given":"Haotong","family":"Qin","sequence":"first","affiliation":[]},{"given":"Xiangguo","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ruihao","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Yifu","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xianglong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"1687_CR1","doi-asserted-by":"crossref","unstructured":"Ajanthan, T., Dokania, P. K., Hartley, R., Torr, P. H. S. (2019). Proximal mean-field for neural network quantization. In IEEE ICCV.","DOI":"10.1109\/ICCV.2019.00497"},{"key":"1687_CR2","unstructured":"akamaster: pytorch_resnet_cifar10. (2021). https:\/\/github.com\/akamaster\/pytorch_resnet_cifar10."},{"key":"1687_CR3","unstructured":"Banner, R., Nahshan, Y., Hoffer, E., & Soudry, D. (2018). Post training 4-bit quantization of convolution networks for rapid-deployment. CoRR arXiv:1810.05723."},{"key":"1687_CR4","unstructured":"Bengio, Y., L\u00e9onard, N., & Courville, A. (2013). Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv."},{"key":"1687_CR5","doi-asserted-by":"crossref","unstructured":"Bethge, J., Bartz, C., Yang, H., Chen, Y., & Meinel, C. (2021). Meliusnet: An improved network architecture for binary neural networks. In WACV.","DOI":"10.1109\/WACV48630.2021.00148"},{"key":"1687_CR6","unstructured":"Bulat, A., & Tzimiropoulos, G. (2019). Xnor-net++: Improved binary neural networks. CoRR arXiv:1909.13863."},{"key":"1687_CR7","unstructured":"Bulat, A., Tzimiropoulos, G., Kossaifi, J., & Pantic, M. (2019). Improved training of binary networks for human pose estimation and image recognition. CoRR arXiv:1904.05868."},{"key":"1687_CR8","doi-asserted-by":"crossref","unstructured":"Cai, Z., He, X., Sun, J., & Vasconcelos, N. (2017). Deep learning with low precision by half-wave gaussian quantization. In IEEE CVPR.","DOI":"10.1109\/CVPR.2017.574"},{"key":"1687_CR9","doi-asserted-by":"crossref","unstructured":"Cao, S., Ma, L., Xiao, W., Zhang, C., Liu, Y., Zhang, L., Nie, L., & Yang, Z. (2019). Seernet: Predicting convolutional neural network feature-map sparsity through low-bit quantization. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.01147"},{"key":"1687_CR10","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhang, B., Zheng, X., Liu, J., Ji, R., Doermann, D., & Guo, G. (2020). Binarized neural architecture search for efficient object recognition. In ICCV.","DOI":"10.1007\/s11263-020-01379-y"},{"key":"1687_CR11","unstructured":"Chen, S., Wang, W., & Pan, S. J. (2019). Metaquant: Learning to quantize by learning to penetrate non-differentiable quantization. In NeurIPS."},{"key":"1687_CR12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, Z., & Wang, N. (2018). Darkrank: Accelerating deep metric learning via cross sample similarities transfer. In AAAI.","DOI":"10.1609\/aaai.v32i1.11783"},{"key":"1687_CR13","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1. CoRR arXiv:1602.02830."},{"key":"1687_CR14","unstructured":"Darabi, S., Belbahri, M., Courbariaux, M., & Nia, V. P. (2018). BNN+: Improved binary network training. CoRR arXiv:1812.11800."},{"key":"1687_CR15","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Li, F. F. (2009). Imagenet: A large-scale hierarchical image database. In IEEE CVPR.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1687_CR16","doi-asserted-by":"publisher","first-page":"106957","DOI":"10.1016\/j.patcog.2019.07.002","volume":"96","author":"H Ding","year":"2019","unstructured":"Ding, H., Chen, K., & Huo, Q. (2019). Compressing CNN-DBLSTM models for OCR with teacher-student learning and tucker decomposition. Pattern Recognition, 96, 106957.","journal-title":"Pattern Recognition"},{"key":"1687_CR17","doi-asserted-by":"crossref","unstructured":"Ding, R., Chin, T. W., Liu, Z., & Marculescu, D. (2019). Regularizing activation distribution for training binarized deep networks. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.01167"},{"issue":"11\u201312","key":"1687_CR18","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1007\/s11263-019-01168-2","volume":"127","author":"Y Dong","year":"2019","unstructured":"Dong, Y., Ni, R., Li, J., Chen, Y., Su, H., & Zhu, J. (2019). Stochastic quantization for learning accurate low-bit deep neural networks. International Journal of Computer Vision, 127(11\u201312), 1629\u20131642.","journal-title":"International Journal of Computer Vision"},{"key":"1687_CR19","doi-asserted-by":"crossref","unstructured":"Dong, Y., Ni, R., Li, J., Chen, Y., Zhu, J., & Su, H. (2017). Learning accurate low-bit deep neural networks with stochastic quantization. BMVC.","DOI":"10.5244\/C.31.189"},{"key":"1687_CR20","doi-asserted-by":"crossref","unstructured":"Dong, Z., Yao, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2019). Hawq: Hessian aware quantization of neural networks with mixed-precision. In IEEE ICCV.","DOI":"10.1109\/ICCV.2019.00038"},{"key":"1687_CR21","doi-asserted-by":"crossref","unstructured":"Everingham, M., Eslami, S. M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98\u2013136.","DOI":"10.1007\/s11263-014-0733-5"},{"issue":"2","key":"1687_CR22","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2), 303\u2013338.","journal-title":"International Journal of Computer Vision"},{"key":"1687_CR23","doi-asserted-by":"crossref","unstructured":"Ge, S., Luo, Z., Zhao, S., Jin, X., & Zhang, X. (2017). Compressing deep neural networks for efficient visual inference. In IEEE ICME.","DOI":"10.1109\/ICME.2017.8019465"},{"key":"1687_CR24","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast r-cnn. In IEEE ICCV.","DOI":"10.1109\/ICCV.2015.169"},{"key":"1687_CR25","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In IEEE CVPR.","DOI":"10.1109\/CVPR.2014.81"},{"key":"1687_CR26","doi-asserted-by":"crossref","unstructured":"Gong, R., Liu, X., Jiang, S., Li, T., Hu, P., Lin, J., Yu, F., & Yan, J. (2019). Differentiable soft quantization: Bridging full-precision and low-bit neural networks. In IEEE ICCV.","DOI":"10.1109\/ICCV.2019.00495"},{"key":"1687_CR27","doi-asserted-by":"crossref","unstructured":"Gu, J., Li, C., Zhang, B., Han, J., Cao, X., Liu, J., & Doermann, D. S. (2018). Projection convolutional neural networks for 1-bit cnns via discrete back propagation. CoRR arXiv:1811.12755.","DOI":"10.1609\/aaai.v33i01.33018344"},{"key":"1687_CR28","doi-asserted-by":"crossref","unstructured":"Gu, J., Zhao, J., Jiang, X., Zhang, B., Liu, J., Guo, G., & Ji, R. (2019). Bayesian optimized 1-bit cnns. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (pp. 4909\u20134917).","DOI":"10.1109\/ICCV.2019.00501"},{"key":"1687_CR29","unstructured":"Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural network with pruning, trained quantization and Huffman coding. ICLR."},{"key":"1687_CR30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In IEEE CVPR.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1687_CR31","doi-asserted-by":"crossref","unstructured":"He, X., Hu, Q., Wang, P., & Cheng, J. (2021). Generative zero-shot network quantization. arXiv preprint arXiv:2101.08430.","DOI":"10.1109\/CVPRW53098.2021.00335"},{"key":"1687_CR32","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X., & Sun, J. (2017). Channel pruning for accelerating very deep neural networks. In IEEE ICCV.","DOI":"10.1109\/ICCV.2017.155"},{"key":"1687_CR33","doi-asserted-by":"crossref","unstructured":"He, Z., & Fan, D. (2019). Simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.01170"},{"key":"1687_CR34","unstructured":"Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2(7)."},{"key":"1687_CR35","unstructured":"Hou, L., Yao, Q., & Kwok, J. T. (2017). Loss-aware binarization of deep networks. ICLR."},{"key":"1687_CR36","unstructured":"Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR arXiv:1704.04861."},{"key":"1687_CR37","doi-asserted-by":"crossref","unstructured":"Hu, Q., Li, G., Wang, P., Zhang, Y., & Cheng, J. (2018). Training binary weight networks via semi-binary decomposition. In ECCV.","DOI":"10.1007\/978-3-030-01261-8_39"},{"key":"1687_CR38","doi-asserted-by":"crossref","unstructured":"Hu, Q., Wang, P., & Cheng T. J. (2018) From hashing to cnns: Training binary weight networks via hashing. In AAAI.","DOI":"10.1609\/aaai.v32i1.11660"},{"key":"1687_CR39","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized neural networks. In NeurIPS ."},{"key":"1687_CR40","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014). Speeding up convolutional neural networks with low rank expansions. In BMVC.","DOI":"10.5244\/C.28.88"},{"key":"1687_CR41","doi-asserted-by":"crossref","unstructured":"Jung, S., Son, C., Lee, S., Son, J., Han, J. J., Kwak, Y., Hwang, S. J., & Choi, C. (2019). Learning to quantize deep networks by optimizing quantization intervals with task loss. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.00448"},{"issue":"9","key":"1687_CR42","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1109\/34.35496","volume":"11","author":"B Kamgar-Parsi","year":"1989","unstructured":"Kamgar-Parsi, B., & Kamgar-Parsi, B. (1989). Evaluation of quantization error in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(9), 929\u2013940. https:\/\/doi.org\/10.1109\/34.35496","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1687_CR43","unstructured":"Krizhevsky, A., Nair, V., & Hinton, G. (2014). The cifar-10 dataset. http:\/\/www.cs.toronto.edu\/kriz\/cifar.html."},{"key":"1687_CR44","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In NeurIPS."},{"key":"1687_CR45","doi-asserted-by":"crossref","unstructured":"Kruger, C. P., & Hancke, G. P. (2014). Benchmarking internet of things devices. In IEEE INDIN (pp. 611\u2013616).","DOI":"10.1109\/INDIN.2014.6945583"},{"key":"1687_CR46","unstructured":"kuangliu, ypwhs, fducau, bearpaw: pytorch-cifar. (2021). https:\/\/github.com\/kuangliu\/pytorch-cifar."},{"key":"1687_CR47","unstructured":"Lahoud, F., Achanta, R., M\u00e1rquez-Neila, P., & S\u00fcsstrunk, S. (2019). Self-binarizing networks. CoRR arXiv:1902.00730."},{"key":"1687_CR48","unstructured":"Lebedev, V., Ganin, Y., Rakhuba, M., Oseledets, I. V., & Lempitsky, V. S. (2015). Speeding-up convolutional neural networks using fine-tuned cp-decomposition. In ICLR."},{"key":"1687_CR49","doi-asserted-by":"crossref","unstructured":"Lebedev, V., & Lempitsky, V. (2016). Fast convnets using group-wise brain damage. In IEEE CVPR.","DOI":"10.1109\/CVPR.2016.280"},{"key":"1687_CR50","unstructured":"Li, F., Zhang, B., & Liu, B. (2016). Ternary weight networks. CoRR arXiv:1605.04711."},{"key":"1687_CR51","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, Y., Liang, F., Qin, H., Yan, J., & Fan, R. (2019). Fully quantized network for object detection. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.00292"},{"key":"1687_CR52","doi-asserted-by":"crossref","unstructured":"Li, Z., Ni, B., Zhang, W., Yang, X., & Gao, W. (2017). Performance guaranteed network acceleration via high-order residual quantization. In IEEE ICCV.","DOI":"10.1109\/ICCV.2017.282"},{"key":"1687_CR53","unstructured":"Lin, J., Gan, C., & Han, S. (2019). Defensive quantization: When efficiency meets robustness. In International Conference on Learning Representations."},{"key":"1687_CR54","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, & P., Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In European Conference on Computer Vision (pp. 740\u2013755). Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"1687_CR55","unstructured":"Lin, X., Zhao, C., & Pan, W. (2017). Towards accurate binary convolutional neural network. In NeurIPS."},{"issue":"4","key":"1687_CR56","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1007\/s11263-020-01417-9","volume":"129","author":"C Liu","year":"2021","unstructured":"Liu, C., Ding, W., Hu, Y., Zhang, B., Liu, J., Guo, G., & Doermann, D. S. (2021). Rectified binary convolutional networks with generative adversarial learning. International Journal of Computer Vision, 129(4), 998\u20131012.","journal-title":"International Journal of Computer Vision"},{"key":"1687_CR57","doi-asserted-by":"crossref","unstructured":"Liu, C., Ding, W., Xia, X., Zhang, B., Gu, J., Liu, J., Ji, R., & Doermann, D. (2019). Circulant binary convolutional networks: Enhancing the performance of 1-bit dcnns with circulant back propagation. In CVPR.","DOI":"10.1109\/CVPR.2019.00280"},{"key":"1687_CR58","unstructured":"Liu, H., Simonyan, K., & Yang, Y. (2018). Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055."},{"key":"1687_CR59","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In European Conference on Computer Vision (pp. 21\u201337). Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"1","key":"1687_CR60","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/s11263-019-01227-8","volume":"128","author":"Z Liu","year":"2020","unstructured":"Liu, Z., Luo, W., Wu, B., Yang, X., Liu, W., & Cheng, K. (2020). Bi-real net: Binarizing deep network towards real-network performance. International Journal of Computer Vision, 128(1), 202\u2013219.","journal-title":"International Journal of Computer Vision"},{"key":"1687_CR61","doi-asserted-by":"crossref","unstructured":"Liu, Z., Shen, Z., Savvides, M., & Cheng, K. T. (2020). Reactnet: Towards precise binary neural network with generalized activation functions. In ECCV.","DOI":"10.1007\/978-3-030-58568-6_9"},{"key":"1687_CR62","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., & Cheng, K. T. (2018). Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm. In ECCV.","DOI":"10.1007\/978-3-030-01267-0_44"},{"key":"1687_CR63","unstructured":"Loshchilov, I., & Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983."},{"key":"1687_CR64","unstructured":"Martinez, B., Yang, J., Bulat, A., & Tzimiropoulos, G. (2020). Training binary neural networks with real-to-binary convolutions. In ICLR."},{"key":"1687_CR65","unstructured":"Mishra, A., Nurvitadhi, E., Cook, J. J., & Marr, D. (2018). WRPN: Wide reduced-precision networks. In ICLR."},{"key":"1687_CR66","doi-asserted-by":"crossref","unstructured":"Morozov, S., & Babenko, A. (2019). Unsupervised neural quantization for compressed-domain similarity search. In IEEE ICCV.","DOI":"10.1109\/ICCV.2019.00313"},{"key":"1687_CR67","doi-asserted-by":"crossref","unstructured":"Nagel, M., Baalen, M. V., Blankevoort, T., & Welling, M. (2019). Data-free quantization through weight equalization and bias correction. In IEEE ICCV.","DOI":"10.1109\/ICCV.2019.00141"},{"key":"1687_CR68","unstructured":"nihui, BUG1989, Howave, gemfield, Corea, eric612: ncnn. (2020). https:\/\/github.com\/Tencent\/ncnn."},{"key":"1687_CR69","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., & Lin, D. (2019). Libra r-cnn: Towards balanced learning for object detection. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"1687_CR70","doi-asserted-by":"crossref","unstructured":"Phan, H., Liu, Z., Huynh, D., Savvides, M., Cheng, K. T., & Shen, Z. (2020). Binarizing mobilenet via evolution-based searching. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01343"},{"key":"1687_CR71","unstructured":"Qin, H., Cai, Z., Zhang, M., Ding, Y., Zhao, H., Yi, S., Liu, X., & Su, H. (2020). Bipointnet: Binary neural network for point clouds. arXiv preprint arXiv:2010.05501."},{"key":"1687_CR72","doi-asserted-by":"crossref","unstructured":"Qin, H., Gong, R., Liu, X., Shen, M., Wei, Z., Yu, F., & Song, J. (2020). Forward and backward information retention for accurate binary neural networks. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00232"},{"key":"1687_CR73","doi-asserted-by":"crossref","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). Xnor-net: Imagenet classification using binary convolutional neural networks. In ECCV.","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"1687_CR74","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In NeurIPS."},{"key":"1687_CR75","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28."},{"key":"1687_CR76","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556."},{"issue":"8","key":"1687_CR77","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1007\/s11263-020-01305-2","volume":"128","author":"J Song","year":"2020","unstructured":"Song, J., He, T., Gao, L., Xu, X., Hanjalic, A., & Shen, H. T. (2020). Unified binary generative adversarial network for image retrieval and compression. International Journal of Computer Vision, 128(8), 2243\u20132264.","journal-title":"International Journal of Computer Vision"},{"key":"1687_CR78","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In IEEE CVPR.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1687_CR79","unstructured":"Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML."},{"key":"1687_CR80","doi-asserted-by":"crossref","unstructured":"Wang, K., Liu, Z., Lin, Y., Lin, J., & Han, S. (2019). Haq: Hardware-aware automated quantization with mixed precision. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.00881"},{"key":"1687_CR81","unstructured":"Wang, P., Chen, Q., He, X., & Cheng, J. (2020). Towards accurate post-training network quantization via bit-split and stitching. In ICML."},{"key":"1687_CR82","doi-asserted-by":"crossref","unstructured":"Wang, P., He, X., Li, G., Zhao, T., & Cheng, J. (2020). Sparsity-inducing binarized neural networks. In AAAI.","DOI":"10.1609\/aaai.v34i07.6900"},{"key":"1687_CR83","doi-asserted-by":"crossref","unstructured":"Wang, Y., Gan, W., Wu, W., & Yan, J. (2019). Dynamic curriculum learning for imbalanced data classification. In IEEE ICCV.","DOI":"10.1109\/ICCV.2019.00512"},{"issue":"10","key":"1687_CR84","doi-asserted-by":"publisher","first-page":"2495","DOI":"10.1109\/TPAMI.2018.2857824","volume":"41","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Xu, C., Xu, C., & Tao, D. (2019). Packing convolutional neural networks in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(10), 2495\u20132510.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1687_CR85","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Lu, J., & Zhou, J. (2020). Bidet: An efficient binarized object detector. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 2049\u20132058).","DOI":"10.1109\/CVPR42600.2020.00212"},{"key":"1687_CR86","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.patcog.2018.04.004","volume":"81","author":"J Wen","year":"2018","unstructured":"Wen, J., Zhang, B., Xu, Y., Yang, J., & Han, N. (2018). Adaptive weighted nonnegative low-rank representation. Pattern Recognition, 81, 326\u2013340.","journal-title":"Pattern Recognition"},{"issue":"9","key":"1687_CR87","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1109\/34.93812","volume":"13","author":"P Wong","year":"1991","unstructured":"Wong, P. (1991). On quantization errors in computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(9), 951\u2013956. https:\/\/doi.org\/10.1109\/34.93812","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1687_CR88","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1109\/TMM.2020.2978593","volume":"23","author":"Y Wu","year":"2020","unstructured":"Wu, Y., Liu, X., Qin, H., Xia, K., Hu, S., Ma, Y., & Wang, M. (2020). Boosting temporal binary coding for large-scale video search. IEEE Transactions on Multimedia, 23, 353\u2013364.","journal-title":"IEEE Transactions on Multimedia"},{"key":"1687_CR89","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wu, Y., Gong, R., Lv, Y., Chen, K., Liang, D., Hu, X., Liu, X., & Yan, J. (2020). Rotation consistent margin loss for efficient low-bit face recognition. In CoRR.","DOI":"10.1109\/CVPR42600.2020.00690"},{"key":"1687_CR90","unstructured":"Xie, B., Liang, Y., & Song, L. (2017). Diverse neural network learns true target functions. In Artificial Intelligence and Statistics."},{"key":"1687_CR91","doi-asserted-by":"crossref","unstructured":"Yang, J., Shen, X., Xing, J., Tian, X., Li, H., Deng, B., Huang, J., & Hua, X. S. (2019). Quantization networks. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.00748"},{"key":"1687_CR92","doi-asserted-by":"crossref","unstructured":"Yu, X., Liu, T., Wang, X., & Tao, D. (2017). On compressing deep models by low rank and sparse decomposition. In IEEE CVPR.","DOI":"10.1109\/CVPR.2017.15"},{"key":"1687_CR93","unstructured":"Zagoruyko, S., & Komodakis, N. (2017). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In ICLR."},{"key":"1687_CR94","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yang, J., Ye, D., & Hua, G. (2018). Lq-nets: Learned quantization for highly accurate and compact deep neural networks. In ECCV.","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"1687_CR95","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pan, Y., Yao, T., Zhao, H., & Mei, T. (2019). dabnn: A super fast inference framework for binary neural networks on ARM devices. In ACM MM.","DOI":"10.1145\/3343031.3350534"},{"key":"1687_CR96","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In IEEE CVPR.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1687_CR97","unstructured":"Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., & Zou, Y. (2016). Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. CoRR arXiv:1606.06160."},{"key":"1687_CR98","unstructured":"Zhu, C., Han, S., Mao, H., & Dally, W. J. (2017). Trained ternary quantization. In ICLR."},{"key":"1687_CR99","doi-asserted-by":"crossref","unstructured":"Zhu, F., Gong, R., Yu, F., Liu, X., Wang, Y., Li, Z., Yang, X., & Yan, J. (2019). Towards unified int8 training for convolutional neural network.","DOI":"10.1109\/CVPR42600.2020.00204"},{"key":"1687_CR100","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2019). Structured binary neural networks for accurate image classification and semantic segmentation. In IEEE CVPR.","DOI":"10.1109\/CVPR.2019.00050"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01687-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-022-01687-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-022-01687-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T03:22:51Z","timestamp":1672975371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-022-01687-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":100,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["1687"],"URL":"https:\/\/doi.org\/10.1007\/s11263-022-01687-5","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,2]]},"assertion":[{"value":"3 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}