{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:03:34Z","timestamp":1777982614393,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach. Intell. Res."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11633-024-1518-0","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T07:24:27Z","timestamp":1746170667000},"page":"499-510","update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SelectQ: Calibration Data Selection for Post-training Quantization"],"prefix":"10.1007","volume":"22","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0002-5703-7969","authenticated-orcid":false,"given":"Zhao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yangcheng","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-9665-0355","authenticated-orcid":false,"given":"Jicong","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Zhongqiu","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Shuicheng","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"1518_CR1","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-030-58568-6_12","volume-title":"Proceedings of the 16th European Conference on Computer Vision","author":"J Philion","year":"2020","unstructured":"J. Philion, S. Fidler. Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3D. In Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK, pp.194\u2013210, 2020. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1007\/978-3-030-58568-6_12."},{"key":"1518_CR2","doi-asserted-by":"publisher","first-page":"2774","DOI":"10.1109\/ICRA48891.2023.10160968","volume-title":"Proceedings of IEEE International Conference on Robotics and Automation","author":"Z J Liu","year":"2023","unstructured":"Z. J. Liu, H. T. Tang, A. Amini, X. Y. Yang, H. Z. Mao, D. L. Rus, S. Han. BEVFusion: Multi-task multi-sensor fusion with unified bird\u2019s-eye view representation. In Proceedings of IEEE International Conference on Robotics and Automation, London, UK, pp. 2774\u20132781, 2023. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/ICRA48891.2023.10160968."},{"key":"1518_CR3","doi-asserted-by":"publisher","first-page":"17161","DOI":"10.1109\/CVPR52688.2022.01667","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Y W Li","year":"2022","unstructured":"Y. W. Li, A. W. Yu, T. J. Meng, B. Caine, J. Q. Ngiam, D. Y. Peng, J. Y. Shen, Y. F. Lu, D. Zhou, Q. V. Le, A. Yuille, M. X. Tan. DeepFusion: Lidar-camera deep fusion for multi-modal 3D object detection. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 17161\u201317170, 2022. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR52688.2022.01667."},{"key":"1518_CR4","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1016\/j.ins.2020.09.003","volume":"546","author":"Y Z Ji","year":"2021","unstructured":"Y. Z. Ji, H. J. Zhang, Z. Zhang, M. Liu. CNN-based encoder-decoder networks for salient object detection: A comprehensive review and recent advances. Information Sciences, vol. 546, pp.835\u2013857, 2021. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1016\/j.ins.2020.09.003.","journal-title":"Information Sciences"},{"key":"1518_CR5","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/ICDM51629.2021.00022","volume-title":"Proceedings of IEEE International Conference on Data Mining","author":"Y C Gao","year":"2021","unstructured":"Y. C. Gao, Z. Zhang, H. J. Zhang, M. B. Zhao, Y. Yang, M. Wang. Dictionary pair-based data-free fast deep neural network compression. In Proceedings of IEEE International Conference on Data Mining, Auckland, New Zealand, pp. 121\u2013130, 2021. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/ICDM51629.2021.00022."},{"key":"1518_CR6","volume-title":"Proceedings of the 8th International Conference on Learning Representations","author":"S K Esser","year":"2020","unstructured":"S. K. Esser, J. L. McKinstry, D. Bablani, R. Appuswamy, D. S. Modha. Learned step size quantization. In Proceedings of the 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, 2020."},{"key":"1518_CR7","doi-asserted-by":"publisher","first-page":"2978","DOI":"10.1109\/CVPRW50498.2020.00356","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","author":"Y Bhalgat","year":"2020","unstructured":"Y. Bhalgat, J. Lee, M. Nagel, T. Blankevoort, N. Kwak. LSQ+: Improving low-bit quantization through learnable offsets and better initialization. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, USA, pp. 2978\u20132985, 2020. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPRW50498.2020.00356."},{"key":"1518_CR8","volume-title":"PACT: Parameterized clipping activation for quantized neural networks","author":"J Choi","year":"2018","unstructured":"J. Choi, Z. Wang, S. Venkataramani, P. I. J. Chuang, V. Srinivasan, K. Gopalakrishnan. PACT: Parameterized clipping activation for quantized neural networks, [Online], Available: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/arxiv.org\/abs\/1805.06085, 2018."},{"key":"1518_CR9","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"R Banner","year":"2019","unstructured":"R. Banner, Y. Nahshan, D. Soudry. Post training 4-bit quantization of convolutional networks for rapid-deployment. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, Article number 714, 2019."},{"key":"1518_CR10","first-page":"7543","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"R Zhao","year":"2019","unstructured":"R. Zhao, Y. W. Hu, J. Dotzel, C. De Sa, Z. R. Zhang. Improving neural network quantization without retraining using outlier channel splitting. In Proceedings of the 36th International Conference on Machine Learning, Long Beach, USA, pp. 7543\u20137552, 2019."},{"key":"1518_CR11","first-page":"7197","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"M Nagel","year":"2020","unstructured":"M. Nagel, R. A. Amjad, M. Van Baalen, C. Louizos, T. Blankevoort. Up or down? Adaptive rounding for post-training quantization. In Proceedings of the 37th International Conference on Machine Learning, pp. 7197\u20137206, 2020."},{"key":"1518_CR12","volume-title":"Proceedings of the 10th International Conference on Learning Representations","author":"X Y Wei","year":"2022","unstructured":"X. Y. Wei, R. H. Gong, Y. H. Li, X. L. Liu, F. W. Yu. QDrop: Randomly dropping quantization for extremely low-bit post-training quantization. In Proceedings of the 10th International Conference on Learning Representations, 2022."},{"key":"1518_CR13","volume-title":"Proceedings of the 9th International Conference on Learning Representations","author":"Y H Li","year":"2021","unstructured":"Y. H. Li, R. H. Gong, X. Tan, Y. Yang, P. Hu, Q. Zhang, F. W. Yu, W. Wang, S. Gu. BRECQ: Pushing the limit of post-training quantization by block reconstruction. In Proceedings of the 9th International Conference on Learning Representations, 2021."},{"key":"1518_CR14","first-page":"4466","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"I Hubara","year":"2021","unstructured":"I. Hubara, Y. Nahshan, Y. Hanani, R. Banner, D. Soudry. Accurate post training quantization with small calibration sets. In Proceedings of the 38th International Conference on Machine Learning, pp. 4466\u20134475, 2021."},{"key":"1518_CR15","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/ISSCC.2014.6757323","volume-title":"Proceedings of IEEE International Solid-State Circuits Conference Digest of Technical Papers","author":"M Horowitz","year":"2014","unstructured":"M. Horowitz. 1.1 computing\u2019s energy problem (and what we can do about it). In Proceedings of IEEE International Solid-State Circuits Conference Digest of Technical Papers, San Francisco, USA, pp. 10\u201314, 2014. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/ISSCC.2014.6757323."},{"key":"1518_CR16","volume-title":"CMSIS-NN: Efficient neural network kernels for arm cortea-M CPUs","author":"L Z Lai","year":"2018","unstructured":"L. Z. Lai, N. Suda, V. Chandra. CMSIS-NN: Efficient neural network kernels for arm cortea-M CPUs, [Online], Available: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/araiv.org\/abs\/1801.06601, 2018."},{"key":"1518_CR17","doi-asserted-by":"publisher","first-page":"13166","DOI":"10.1109\/CVPR42600.2020.01318","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Y H Cai","year":"2020","unstructured":"Y. H. Cai, Z. W. Yao, Z. Dong, A. Gholami, M. W. Mahoney, K. Keutzer. ZeroQ: A novel zero shot quantization framework. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, pp. 13166\u201313175, 2020. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR42600.2020.01318."},{"key":"1518_CR18","doi-asserted-by":"publisher","first-page":"15653","DOI":"10.1109\/CVPR46437.2021.01540","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"X G Zhang","year":"2021","unstructured":"X. G. Zhang, H. T. Qin, Y. F. Ding, R. H. Gong, Q. H. Yan, R. S. Tao, Y. H. Li, F. W. Yu, X. L. Liu. Diversifying sample generation for accurate data-free quantization. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, pp. 15653\u201315662, 2021. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR46437.2021.01540."},{"key":"1518_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_1","volume-title":"Proceedings of the 16th European Conference on Computer Vision","author":"S K Xu","year":"2020","unstructured":"S. K. Xu, H. K. Li, B. H. Zhuang, J. Liu, J. Z. Cao, C. R. Liang, M. K. Tan. Generative low-bitwidth data free quantization. In Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK, 2020. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1007\/978-3-030-58610-2_1."},{"key":"1518_CR20","doi-asserted-by":"publisher","first-page":"12329","DOI":"10.1109\/CVPR52688.2022.01202","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Y S Zhong","year":"2022","unstructured":"Y. S. Zhong, M. B. Lin, G. R. Nan, J. Z. Liu, B. C. Zhang, Y. H. Tian, R. R. Ji. IntraQ: Learning synthetic images with intra-class heterogeneity for zero-shot network quantization. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 12329\u201312338, 2022. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR52688.2022.01202."},{"key":"1518_CR21","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/ICDM54844.2022.00024","volume-title":"Proceedings of IEEE International Conference on Data Mining","author":"Y C Gao","year":"2022","unstructured":"Y. C. Gao, Z. Zhang, R. C. Hong, H. J. Zhang, J. C. Fan, S. C. Yan. Towards feature distribution alignment and diversity enhancement for data-free quantization. In Proceedings of IEEE International Conference on Data Mining, Orlando, USA, pp. 141\u2013150, 2022. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/ICDM54844.2022.00024."},{"key":"1518_CR22","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","author":"K M He","year":"2016","unstructured":"K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 770\u2013778, 2016. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR.2016.90."},{"key":"1518_CR23","doi-asserted-by":"publisher","first-page":"7132","DOI":"10.1109\/CVPR.2018.00745","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"J Hu","year":"2018","unstructured":"J. Hu, L. Shen, G. Sun. Squeeze-and-excitation networks. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 7132\u20137141, 2018. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR.2018.00745."},{"key":"1518_CR24","doi-asserted-by":"publisher","first-page":"4510","DOI":"10.1109\/CVPR.2018.00474","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"M Sandler","year":"2018","unstructured":"M. Sandler, A. Howard, M. L. Zhu, A. Zhmoginov, L. C. Chen. MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 4510\u20134520, 2018. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR.2018.00474."},{"key":"1518_CR25","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1109\/ICCV.2019.00140","volume-title":"Proceedings of IEEE\/CVF International Conference on Computer Vision","author":"A Howard","year":"2019","unstructured":"A. Howard, M. Sandler, B. Chen, W. J. Wang, L. C. Chen, M. X. Tan, G. Chu, V. Vasudevan, Y. K. Zhu, R. M. Pang, H. Adam, Q. Le. Searching for mobileNetV3. In Proceedings of IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea, pp. 1314\u20131324, 2019. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/ICCV.2019.00140."},{"key":"1518_CR26","doi-asserted-by":"publisher","first-page":"6848","DOI":"10.1109\/CVPR.2018.00716","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"X Y Zhang","year":"2018","unstructured":"X. Y. Zhang, X. Y. Zhou, M. X. Lin, J. Sun. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, pp. 6848\u20136856, 2018. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR.2018.00716."},{"key":"1518_CR27","volume-title":"SqueezeNet: Aleanet-level accuracy with 50x fewer parameters and < 0.5 MB model size","author":"F N Iandola","year":"2016","unstructured":"F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, K. Keutzer. SqueezeNet: Aleanet-level accuracy with 50x fewer parameters and < 0.5 MB model size, [Online], Available: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/arxiv.org\/abs\/1602.07360, 2016."},{"key":"1518_CR28","doi-asserted-by":"publisher","first-page":"2815","DOI":"10.1109\/CVPR.2019.00293","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"M X Tan","year":"2019","unstructured":"M. X. Tan, B. Chen, R. M. Pang, V. Vasudevan, M. Sandler, A. Howard, Q. V. Le. MnasNet: Platform-aware neural architecture search for mobile. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, pp. 2815\u20132823, 2019. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR.2019.00293."},{"key":"1518_CR29","volume-title":"Estimating or propagating gradients through stochastic neurons for conditional computation","author":"Y Bengio","year":"2013","unstructured":"Y. Bengio, N. L\u00e9onard, A. Courville. Estimating or propagating gradients through stochastic neurons for conditional computation, [Online], Available: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/arxiv.org\/abs\/1308.3432, 2013."},{"key":"1518_CR30","volume-title":"Proceedings of the 10th International Conference on Learning Representations","author":"C Guo","year":"2022","unstructured":"C. Guo, Y. X. Qiu, J. W. Leng, X. T. Gao, C. Zhang, Y. X. Liu, F. Yang, Y. H. Zhu, M. Y. Guo. SQuant: On-the-fly data-free quantization via diagonal hessian approximation. In Proceedings of the 10th International Conference on Learning Representations, 2022."},{"key":"1518_CR31","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/978-3-031-20083-0_5","volume-title":"In Proceedings of the 17th European Conference on Computer Vision","author":"Y S Zhong","year":"2022","unstructured":"Y. S. Zhong, M. B. Lin, M. Z. Chen, K. Li, Y. H. Shen, F. Chao, Y. J. Wu, R. R. Ji. Fine-grained data distribution alignment for post-training quantization. In Proceedings of the 17th European Conference on Computer Vision, Tel Aviv, Israel, pp. 70\u201386, 2022. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1007\/978-3-031-20083-0_5."},{"issue":"3","key":"1518_CR32","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. A. Ma, Z. H. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, F. F. Li. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, vol. 115, no. 3, pp. 211\u2013252, 2015. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1007\/s11263-015-0816-y.","journal-title":"International Journal of Computer Vision"},{"key":"1518_CR33","doi-asserted-by":"publisher","first-page":"12319","DOI":"10.1109\/CVPR52688.2022.01201","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Y Jeon","year":"2022","unstructured":"Y. Jeon, C. Lee, E. Cho, Y. Ro. Mr.BIQ: Post-training non-uniform quantization based on minimizing the reconstruction error. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 12319\u201312328, 2022. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR52688.2022.01201."},{"key":"1518_CR34","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"S Han","year":"2016","unstructured":"S. Han, H. Z. Mao, W. J. Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, [Online], Available: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/arxiv.org\/abs\/1510.00149, 2016."},{"key":"1518_CR35","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/978-3-030-01237-3_23","volume-title":"Proceedings of the 15th European Conference on Computer Vision","author":"D Q Zhang","year":"2018","unstructured":"D. Q. Zhang, J. L. Yang, D. Q. Z. Ye, G. Hua. LQ-Nets: Learned quantization for highly accurate and compact deep neural networks. In Proceedings of the 15th European Conference on Computer Vision, Munich, Germany, pp. 373\u2013390, 2018. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1007\/978-3-030-01237-3_23."},{"key":"1518_CR36","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489496","volume-title":"Proceedings of International Joint Conference on Neural Networks","author":"Z Qin","year":"2018","unstructured":"Z. Qin, Z. N. Zhang, S. Q. Zhang, H. Yu, J. C. Li, Y. X. Peng. Merging and evolution: Improving convolutional neural networks for mobile applications. In Proceedings of International Joint Conference on Neural Networks, Rio de Janeiro, Brazil, 2018. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/IJCNN.2018.8489496."},{"key":"1518_CR37","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"A Paszke","year":"2019","unstructured":"A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. M. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. K\u00f6pf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. J. Bai, S. Chintala. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Canada, Article number 721, 2019."},{"key":"1518_CR38","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1109\/CVPR52688.2022.00194","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Z Zhang","year":"2022","unstructured":"Z. Zhang, H. Zheng, R. C. Hong, M. L. Xu, S. C. Yan, M. Wang. Deep color consistent network for low-light image enhancement. In Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, pp. 1889\u20131898, 2022. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/CVPR52688.2022.00194."},{"key":"1518_CR39","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TIP.2021.3074804","volume":"30","author":"Y Y Wei","year":"2021","unstructured":"Y. Y. Wei, Z. Zhang, Y. Wang, M. L. Xu, Y. Yang, S. C. Yan, M. Wang. DerainCycleGAN: Rain attentive CycleGAN for single image deraining and rainmaking. IEEE Transactions on Image Processing, vol. 30, pp. 4788\u20134801, 2021. DOI: https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1109\/TIP.2021.3074804.","journal-title":"IEEE Transactions on Image Processing"}],"container-title":["Machine Intelligence Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11633-024-1518-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s11633-024-1518-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11633-024-1518-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T02:15:18Z","timestamp":1748312118000},"score":1,"resource":{"primary":{"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/link.springer.com\/10.1007\/s11633-024-1518-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,30]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1518"],"URL":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1007\/s11633-024-1518-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.21456291.v1","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.21456291.v2","asserted-by":"object"}]},"ISSN":["2731-538X","2731-5398"],"issn-type":[{"value":"2731-538X","type":"print"},{"value":"2731-5398","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,30]]},"assertion":[{"value":"23 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declared that they have no conflicts of interest to this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations of conflict of interest"}}]}}