{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T18:41:48Z","timestamp":1770489708122,"version":"3.49.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T00:00:00Z","timestamp":1635120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["No. 61971388"],"award-info":[{"award-number":["No. 61971388"]}],"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":["U1706218"],"award-info":[{"award-number":["U1706218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"key natural science foundation of shandong","award":["No. ZR2018ZB0852"],"award-info":[{"award-number":["No. ZR2018ZB0852"]}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["L1824025"],"award-info":[{"award-number":["L1824025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2022,5]]},"DOI":"10.1007\/s13042-021-01443-0","type":"journal-article","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T19:02:36Z","timestamp":1635188556000},"page":"1213-1230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Dual discriminator adversarial distillation for data-free model compression"],"prefix":"10.1007","volume":"13","author":[{"given":"Haoran","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1870-9037","authenticated-orcid":false,"given":"Xin","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Milos","family":"Manic","sequence":"additional","affiliation":[]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,25]]},"reference":[{"key":"1443_CR1","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1443_CR2","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1109\/TIP.2020.3043128","volume":"30","author":"X Li","year":"2021","unstructured":"Li X, Wu J, Sun Z, Ma Z, Cao J, Xue JH (2021a) Bsnet: Bi-similarity network for few-shot fine-grained image classification. IEEE Trans Image Process 30:1318\u20131331","journal-title":"IEEE Trans Image Process"},{"key":"1443_CR3","doi-asserted-by":"publisher","first-page":"4735","DOI":"10.1109\/TIP.2021.3066051","volume":"30","author":"X Li","year":"2021","unstructured":"Li X, Li S, Omar B, Wu F, Li X (2021b) Reskd: Residual-guided knowledge distillation. IEEE Trans Image Process 30:4735\u20134746","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"1443_CR4","doi-asserted-by":"publisher","first-page":"3588","DOI":"10.1109\/TIE.2020.2977553","volume":"68","author":"X Sun","year":"2021","unstructured":"Sun X, Xv H, Dong J, Zhou H, Chen C, Li Q (2021) Few-shot learning for domain-specific fine-grained image classification. IEEE Trans Ind Electron 68(4):3588\u20133598","journal-title":"IEEE Trans Ind Electron"},{"key":"1443_CR5","unstructured":"Ren S, He K, Girshick RB, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems. Quebec, Canada, pp 91\u201399"},{"issue":"3","key":"1443_CR6","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1109\/TMM.2019.2933338","volume":"22","author":"J Lou","year":"2020","unstructured":"Lou J, Wang Y, Nduka C, Hamedi M, Mavridou I, Wang F, Yu H (2020) Realistic facial expression reconstruction for VR HMD users. IEEE Trans Multim 22(3):730\u2013743","journal-title":"IEEE Trans Multim"},{"key":"1443_CR7","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/TIP.2020.3031371","volume":"30","author":"M Feng","year":"2021","unstructured":"Feng M, Gilani SZ, Wang Y, Zhang L, Mian A (2021) Relation graph network for 3d object detection in point clouds. IEEE Trans Image Process 30:92\u2013107","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"1443_CR8","doi-asserted-by":"publisher","first-page":"3078","DOI":"10.1109\/TCSVT.2020.3035108","volume":"31","author":"L Chen","year":"2020","unstructured":"Chen L, Jiang Z, Tong L, Liu Z, Zhao A, Zhang Q, Dong J, Zhou H (2020) Perceptual underwater image enhancement with deep learning and physical priors. IEEE Trans Circuits Syst Video Technol 31(8):3078\u20133092","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1443_CR9","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1443_CR10","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.neucom.2020.12.089","volume":"438","author":"Y Ming","year":"2021","unstructured":"Ming Y, Meng X, Fan C, Yu H (2021) Deep learning for monocular depth estimation: a review. Neurocomputing 438:14\u201333","journal-title":"Neurocomputing"},{"issue":"07","key":"1443_CR11","first-page":"10510","volume":"34","author":"C Chen","year":"2020","unstructured":"Chen C, Sun X, Hua Y, Dong J, Xv H (2020) Learning deep relations to promote saliency detection. Proc AAAI Conf Artif Intell 34(07):10510\u201310517","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"1443_CR12","doi-asserted-by":"publisher","first-page":"107940","DOI":"10.1016\/j.patcog.2021.107940","volume":"115","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Sun X, Dong J, Chen C, Lv Q (2021) Gpnet: Gated pyramid network for semantic segmentation. Pattern Recognit 115:107940","journal-title":"Pattern Recognit"},{"key":"1443_CR13","doi-asserted-by":"publisher","unstructured":"Lv Q, Sun X, Chen C, Dong J, Zhou H (2021) Parallel complement network for real-time semantic segmentation of road scenes. In: IEEE Transactions on Intelligent Transportation Systems. https:\/\/doi.org\/10.1109\/TITS.2020.3044672","DOI":"10.1109\/TITS.2020.3044672"},{"key":"1443_CR14","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1109\/TIP.2020.3042065","volume":"30","author":"T Wu","year":"2021","unstructured":"Wu T, Tang S, Zhang R, Cao J, Zhang Y (2021) Cgnet: a light-weight context guided network for semantic segmentation. IEEE Trans Image Process 30:1169\u20131179","journal-title":"IEEE Trans Image Process"},{"key":"1443_CR15","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TIP.2019.2936742","volume":"29","author":"X Han","year":"2020","unstructured":"Han X, Song X, Yao Y, Xu XS, Nie L (2020) Neural compatibility modeling with probabilistic knowledge distillation. IEEE Trans Image Process 29:871\u2013882","journal-title":"IEEE Trans Image Process"},{"key":"1443_CR16","doi-asserted-by":"publisher","first-page":"2963","DOI":"10.1109\/TIP.2021.3056895","volume":"30","author":"C Bian","year":"2021","unstructured":"Bian C, Feng W, Wan L, Wang S (2021) Structural knowledge distillation for efficient skeleton-based action recognition. IEEE Trans Image Process 30:2963\u20132976","journal-title":"IEEE Trans Image Process"},{"issue":"11","key":"1443_CR17","doi-asserted-by":"publisher","first-page":"4207","DOI":"10.1109\/TCSVT.2019.2952779","volume":"30","author":"A Mart\u00ednez-Gonz\u00e1lez","year":"2020","unstructured":"Mart\u00ednez-Gonz\u00e1lez A, Villamizar M, Can\u00e9vet O, Odobez JM (2020) Efficient convolutional neural networks for depth-based multi-person pose estimation. IEEE Trans Circuits Syst Video Technol 30(11):4207\u20134221","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"5","key":"1443_CR18","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1109\/TCSVT.2020.2982505","volume":"32","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Wang R, Chen Y, Jia W, Liu X, Gao W (2021) Lighter but efficient bit-depth expansion network. IEEE Trans Circuits Syst Video Technol 32(5):2063\u20132069","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"4","key":"1443_CR19","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1109\/TCSVT.2020.3005311","volume":"31","author":"DS Tan","year":"2021","unstructured":"Tan DS, Lin Y, Hua K (2021) Incremental learning of multi-domain image-to-image translations. IEEE Trans Circuits Syst Video Technol 31(4):1526\u20131539","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1443_CR20","unstructured":"Gupta S, Agrawal A, Gopalakrishnan K, Narayanan P (2015) Deep learning with limited numerical precision. In: Proceedings of the 32nd International Conference on Machine Learning, ICML2015, Lille, France, 6-11 July, pp 1737\u20131746"},{"issue":"2","key":"1443_CR21","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1109\/TCSVT.2020.2991171","volume":"31","author":"H Zhai","year":"2021","unstructured":"Zhai H, Lai S, Jin H, Qian X, Mei T (2021) Deep transfer hashing for image retrieval. IEEE Trans Circuits Syst Video Technol 31(2):742\u2013753","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1443_CR22","unstructured":"Han S, Pool J, Tran J, Dally WJ (2015) Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, Quebec, Canada, December 7-12,, pp 1135\u20131143"},{"issue":"3","key":"1443_CR23","doi-asserted-by":"publisher","first-page":"1114","DOI":"10.1109\/TCSVT.2020.2996231","volume":"31","author":"J Guo","year":"2021","unstructured":"Guo J, Zhang W, Ouyang W, Xu D (2021) Model compression using progressive channel pruning. IEEE Trans Circuits Syst Video Technol 31(3):1114\u20131124","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"7","key":"1443_CR24","first-page":"38","volume":"14","author":"G Hinton","year":"2015","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. Comput Sci 14(7):38\u201339","journal-title":"Comput Sci"},{"issue":"7","key":"1443_CR25","first-page":"2093","volume":"30","author":"HJ Kang","year":"2020","unstructured":"Kang HJ (2020) Accelerator-aware pruning for convolutional neural networks. IEEE Trans Circuits Syst Video Technol 30(7):2093\u20132103","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1443_CR26","doi-asserted-by":"publisher","unstructured":"Liu T, Lam KM, Zhao R, Qiu G (2021) Deep cross-modal representation learning and distillation for illumination-invariant pedestrian detection. IEEE Transactions on Circuits and Systems for Video Technology. https:\/\/doi.org\/10.1109\/TCSVT.2021.3060162","DOI":"10.1109\/TCSVT.2021.3060162"},{"key":"1443_CR27","unstructured":"Romero A, Ballas N, Kahou SE, Chassang A, Bengio Y (2015) Fitnets: Hints for thin deep nets. In: 3rd International Conference on Learning Representations, ICLR"},{"key":"1443_CR28","unstructured":"Zagoruyko S, Komodakis N (2017) Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In: 5th International Conference on Learning Representations, ICLR"},{"key":"1443_CR29","doi-asserted-by":"crossref","unstructured":"Yim J, Joo D, Bae J, Kim J (2017) A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp 7130\u20137138","DOI":"10.1109\/CVPR.2017.754"},{"key":"1443_CR30","doi-asserted-by":"crossref","unstructured":"Park W, Kim D, Lu Y, Cho M (2019) Relational knowledge distillation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3967\u20133976","DOI":"10.1109\/CVPR.2019.00409"},{"key":"1443_CR31","doi-asserted-by":"crossref","unstructured":"Tung F, Mori G (2019) Similarity-preserving knowledge distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1365\u20131374","DOI":"10.1109\/ICCV.2019.00145"},{"key":"1443_CR32","doi-asserted-by":"crossref","unstructured":"Chen H, Wang Y, Xu C, Yang Z, Liu C, Shi B, Xu C, Xu C, Tian Q (2019) Data-free learning of student networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3514\u20133522","DOI":"10.1109\/ICCV.2019.00361"},{"key":"1443_CR33","unstructured":"Micaelli P, Storkey AJ (2019) Zero-shot knowledge transfer via adversarial belief matching. In: Advances in Neural Information Processing Systems, pp 9551\u20139561"},{"key":"1443_CR34","unstructured":"Nayak GK, Mopuri KR, Shaj V, Radhakrishnan VB, Chakraborty A (2019) Zero-shot knowledge distillation in deep networks. In: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, California, USA, 9-15 June, pp 4743\u20134751"},{"key":"1443_CR35","unstructured":"Lopes RG, Fenu S, Starner T (2017) Data-free knowledge distillation for deep neural networks. arXiv preprint: arXiv:1710.07535"},{"key":"1443_CR36","unstructured":"Yoo J, Cho M, Kim T, Kang U (2019) Knowledge extraction with no observable data. In: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Vancouver, BC, Canada, December 8-14, pp 2701\u20132710"},{"key":"1443_CR37","doi-asserted-by":"crossref","unstructured":"Yin H, Molchanov P, Alvarez JM, Li Z, Mallya A, Hoiem D, Jha NK, Kautz J (2020) Dreaming to distill: Data-free knowledge transfer via deepinversion. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, pp 8712\u20138721","DOI":"10.1109\/CVPR42600.2020.00874"},{"key":"1443_CR38","unstructured":"Fang G, Song J, Shen C, Wang X, Chen D, Song M (2019) Data-free adversarial distillation. arXiv preprint: arXiv:1912.11006"},{"key":"1443_CR39","doi-asserted-by":"crossref","unstructured":"Haroush M, Hubara I, Hoffer E, Soudry D (2020) The knowledge within: Methods for data-free model compression. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, pp 8491\u20138499","DOI":"10.1109\/CVPR42600.2020.00852"},{"key":"1443_CR40","unstructured":"Mordvintsev A, Olah C, Tyka M (2015) Inceptionism: Going deeper into neural networks. Tech. rep, Google AI"},{"key":"1443_CR41","doi-asserted-by":"crossref","unstructured":"Bucila C, Caruana R, Niculescu-Mizil A (2006) Model compression. In: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20-23, pp 535\u2013541","DOI":"10.1145\/1150402.1150464"},{"key":"1443_CR42","unstructured":"Ba J, Caruana R (2014) Do deep nets really need to be deep? In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, Quebec, Canada, December 8-13, pp 2654\u20132662"},{"key":"1443_CR43","doi-asserted-by":"publisher","unstructured":"Zhao H, Sun X, Dong J, Chen C, Dong Z (2020) Highlight every step: Knowledge distillation via collaborative teaching. In: IEEE Transactions on Cybernetics. https:\/\/doi.org\/10.1109\/TCYB.2020.3007506","DOI":"10.1109\/TCYB.2020.3007506"},{"key":"1443_CR44","doi-asserted-by":"crossref","unstructured":"Deng J, Pan Y, Yao T, Zhou W, Li H, Mei T (2019) Relation distillation networks for video object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, pp 7022\u20137031","DOI":"10.1109\/ICCV.2019.00712"},{"key":"1443_CR45","doi-asserted-by":"crossref","unstructured":"Liu Y, Chen K, Liu C, Qin Z, Luo Z, Wang J (2019) Structured knowledge distillation for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, pp 2604\u20132613","DOI":"10.1109\/CVPR.2019.00271"},{"key":"1443_CR46","doi-asserted-by":"crossref","unstructured":"Hou Y, Ma Z, Liu C, Loy CC (2019) Learning lightweight lane detection cnns by self attention distillation. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, pp 1013\u20131021","DOI":"10.1109\/ICCV.2019.00110"},{"issue":"8","key":"1443_CR47","doi-asserted-by":"publisher","first-page":"3093","DOI":"10.1109\/TCSVT.2020.3035890","volume":"31","author":"H Liu","year":"2021","unstructured":"Liu H, Zhu X, Lei Z, Cao D, Li SZ (2021) Fast adapting without forgetting for face recognition. IEEE Trans Circuits Syst Video Technol 31(8):3093\u20133104","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1443_CR48","doi-asserted-by":"publisher","first-page":"6898","DOI":"10.1109\/TIP.2020.2995049","volume":"29","author":"S Ge","year":"2020","unstructured":"Ge S, Zhao S, Li C, Zhang Y, Li J (2020) Efficient low-resolution face recognition via bridge distillation. IEEE Trans Image Process 29:6898\u20136908","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"1443_CR49","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TIP.2018.2883743","volume":"28","author":"S Ge","year":"2019","unstructured":"Ge S, Zhao S, Li C, Li J (2019) Low-resolution face recognition in the wild via selective knowledge distillation. IEEE Trans Image Process 28(4):2051\u20132062","journal-title":"IEEE Trans Image Process"},{"issue":"9","key":"1443_CR50","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TCSVT.2020.2973301","volume":"30","author":"Y Tang","year":"2020","unstructured":"Tang Y, Wei Y, Yu X, Lu J, Zhou J (2020) Graph interaction networks for relation transfer in human activity videos. IEEE Trans Circuits Syst Video Technol 30(9):2872\u20132886","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"1443_CR51","doi-asserted-by":"crossref","unstructured":"Li T, Li J, Liu Z, Zhang C (2020) Few sample knowledge distillation for efficient network compression. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, pp 14627\u201314635","DOI":"10.1109\/CVPR42600.2020.01465"},{"key":"1443_CR52","doi-asserted-by":"crossref","unstructured":"Bai H, Wu J, King I, Lyu MR (2020) Few shot network compression via cross distillation. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, pp 3203\u20133210","DOI":"10.1609\/aaai.v34i04.5718"},{"key":"1443_CR53","doi-asserted-by":"crossref","unstructured":"Wang D, Li Y, Wang L, Gong B (2020) Neural networks are more productive teachers than human raters: Active mixup for data-efficient knowledge distillation from a blackbox model. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, pp 1495\u20131504","DOI":"10.1109\/CVPR42600.2020.00157"},{"key":"1443_CR54","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville AC, Bengio Y (2014) Generative adversarial nets. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, Montreal, Quebec, Canada, December 8-13, pp 2672\u20132680"},{"key":"1443_CR55","unstructured":"Liu R, Fusi N, Mackey L (2018) Model compression with generative adversarial networks. arXiv preprint: arXiv:1812.02271"},{"key":"1443_CR56","unstructured":"Chung I, Park S, Kim J, Kwak N (2020) Feature-map-level online adversarial knowledge distillation. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, pp 2006\u20132015"},{"key":"1443_CR57","unstructured":"Nguyen TD, Le T, Vu H, Phung DQ (2017) Dual discriminator generative adversarial nets. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp 2670\u20132680"},{"key":"1443_CR58","doi-asserted-by":"crossref","unstructured":"Choi Y, Choi JP, El-Khamy M, Lee J (2020) Data-free network quantization with adversarial knowledge distillation. arXiv preprint: arXiv:2005.04136","DOI":"10.1109\/CVPRW50498.2020.00363"},{"key":"1443_CR59","doi-asserted-by":"crossref","unstructured":"Fang G, Song J, Wang X, Shen C, Wang X, Song M (2021) Contrastive model inversion for data-free knowledge distillation. arXiv preprint: arXiv:2105.08584","DOI":"10.24963\/ijcai.2021\/327"},{"key":"1443_CR60","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"1443_CR61","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein MS, Berg AC, Li F (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"1443_CR62","doi-asserted-by":"crossref","unstructured":"Brostow GJ, Shotton J, Fauqueur J, Cipolla R (2008) Segmentation and recognition using structure from motion point clouds. In: European conference on computer vision, Springer, pp 44\u201357","DOI":"10.1007\/978-3-540-88682-2_5"},{"key":"1443_CR63","doi-asserted-by":"crossref","unstructured":"Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: European conference on computer vision, Springer, pp 746\u2013760","DOI":"10.1007\/978-3-642-33715-4_54"},{"key":"1443_CR64","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pp 3213\u20133223","DOI":"10.1109\/CVPR.2016.350"},{"issue":"2","key":"1443_CR65","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Gool LV, Williams CKI, Winn JM, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"key":"1443_CR66","unstructured":"Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. In: 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01443-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01443-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01443-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T08:47:03Z","timestamp":1649321223000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01443-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,25]]},"references-count":66,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,5]]}},"alternative-id":["1443"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01443-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,25]]},"assertion":[{"value":"23 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}