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In this regard, we propose an active domain adaptation method named Boosting Active Domain Adaptation with Exploration of Samples (BADA), dividing Active DA into two related issues: sample selection and sample utilization. We design the instability selection criterion based on predictive consistency and the diversity selection criterion. For the remaining unlabeled samples, we design a self-training framework, which screens out reliable samples and unreliable samples through the sample screening mechanism similar to selection criteria. And we adopt respective loss functions for reliable samples and unreliable samples. Experiments show that BADA remarkably outperforms previous active learning methods and Active DA methods on several domain adaptation datasets.<\/jats:p>","DOI":"10.3233\/ida-230150","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T15:12:57Z","timestamp":1694790777000},"page":"667-683","source":"Crossref","is-referenced-by-count":1,"title":["Boosting active domain adaptation with exploration of samples"],"prefix":"10.1177","volume":"28","author":[{"given":"Qing","family":"Tian","sequence":"first","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China"},{"name":"Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China"},{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-230150_ref2","doi-asserted-by":"crossref","unstructured":"G. Huang, Z. Liu, L. Van Der Maaten and K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp.\u00a04700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"4","key":"10.3233\/IDA-230150_ref3","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/IDA-230150_ref4","doi-asserted-by":"crossref","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff and H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp.\u00a0801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.3233\/IDA-230150_ref5","doi-asserted-by":"crossref","unstructured":"K. Saenko, B. Kulis, M. Fritz and T. Darrell, Adapting visual category models to new domains, in: Computer Vision-ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5\u201311, 2010, Proceedings, Part IV 11, Springer, 2010, pp.\u00a0213\u2013226.","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"10.3233\/IDA-230150_ref6","unstructured":"Y. Ganin and V. Lempitsky, Unsupervised domain adaptation by backpropagation, in: International Conference on Machine Learning, PMLR, 2015, pp.\u00a01180\u20131189."},{"key":"10.3233\/IDA-230150_ref7","unstructured":"M. Long, Z. Cao, J. Wang and M.I. Jordan, Conditional adversarial domain adaptation, Advances in Neural Information Processing Systems 31 (2018)."},{"key":"10.3233\/IDA-230150_ref8","doi-asserted-by":"crossref","unstructured":"Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang and M. Chandraker, Learning to adapt structured output space for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7472\u20137481.","DOI":"10.1109\/CVPR.2018.00780"},{"key":"10.3233\/IDA-230150_ref9","unstructured":"P. Rai, A. Saha, H. Daum\u00e9\u00a0III and S. Venkatasubramanian, Domain adaptation meets active learning, in: Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing, 2010, pp.\u00a027\u201332."},{"key":"10.3233\/IDA-230150_ref12","unstructured":"Y. Gal, R. Islam and Z. Ghahramani, Deep bayesian active learning with image data, in: International Conference on Machine Learning, PMLR, 2017, pp.\u00a01183\u20131192."},{"key":"10.3233\/IDA-230150_ref13","unstructured":"A. Kirsch, J. Van Amersfoort and Y. Gal, Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning, Advances in Neural Information Processing Systems 32 (2019)."},{"key":"10.3233\/IDA-230150_ref14","doi-asserted-by":"crossref","unstructured":"S.-J. Huang, J.-W. Zhao and Z.-Y. Liu, Cost-effective training of deep cnns with active model adaptation, in: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp.\u00a01580\u20131588.","DOI":"10.1145\/3219819.3220026"},{"key":"10.3233\/IDA-230150_ref15","unstructured":"Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan and J. Snoek, Can you trust your model\u2019s uncertainty? evaluating predictive uncertainty under dataset shift, Advances in Neural Information Processing Systems 32 (2019)."},{"key":"10.3233\/IDA-230150_ref16","unstructured":"J.-C. Su, Y.-H. Tsai, K. Sohn, B. Liu, S. Maji and M. Chandraker, Active adversarial domain adaptation, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2020, pp.\u00a0739\u2013748."},{"issue":"1","key":"10.3233\/IDA-230150_ref17","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative adversarial networks: An overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Processing Magazine"},{"key":"10.3233\/IDA-230150_ref18","doi-asserted-by":"crossref","unstructured":"B. Fu, Z. Cao, J. Wang and M. Long, Transferable query selection for active domain adaptation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp.\u00a07272\u20137281.","DOI":"10.1109\/CVPR46437.2021.00719"},{"key":"10.3233\/IDA-230150_ref19","doi-asserted-by":"crossref","unstructured":"H. Rangwani, A. Jain, S.K. Aithal and R.V. Babu, S3vaada: Submodular subset selection for virtual adversarial active domain adaptation, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp.\u00a07516\u20137525.","DOI":"10.1109\/ICCV48922.2021.00742"},{"issue":"1","key":"10.3233\/IDA-230150_ref20","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"The Journal of Machine Learning Research"},{"key":"10.3233\/IDA-230150_ref22","doi-asserted-by":"crossref","unstructured":"W. Zhang, W. Ouyang, W. Li and D. Xu, Collaborative and adversarial network for unsupervised domain adaptation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp.\u00a03801\u20133809.","DOI":"10.1109\/CVPR.2018.00400"},{"key":"10.3233\/IDA-230150_ref23","doi-asserted-by":"crossref","unstructured":"O. Chapelle and A. Zien, Semi-supervised classification by low density separation, in: International Workshop on Artificial Intelligence and Statistics, PMLR, 2005, pp.\u00a057\u201364.","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"10.3233\/IDA-230150_ref24","doi-asserted-by":"crossref","unstructured":"S. Cicek and S. Soatto, Unsupervised domain adaptation via regularized conditional alignment, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp.\u00a01416\u20131425.","DOI":"10.1109\/ICCV.2019.00150"},{"key":"10.3233\/IDA-230150_ref25","unstructured":"A. Rastrow, F. Jelinek, A. Sethy and B. Ramabhadran, Unsupervised model adaptation using information-theoretic criterion, in: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010, pp.\u00a0190\u2013197."},{"key":"10.3233\/IDA-230150_ref26","doi-asserted-by":"crossref","unstructured":"S. Tan, X. Peng and K. Saenko, Class-imbalanced domain adaptation: an empirical odyssey, in: Computer Vision-ECCV 2020 Workshops: Glasgow, UK, August 23\u201328, 2020, Proceedings, Part I 16, Springer, 2020, pp.\u00a0585\u2013602.","DOI":"10.1007\/978-3-030-66415-2_38"},{"key":"10.3233\/IDA-230150_ref27","doi-asserted-by":"crossref","unstructured":"Y. Zou, Z. Yu, X. Liu, B. Kumar and J. Wang, Confidence regularized self-training, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp.\u00a05982\u20135991.","DOI":"10.1109\/ICCV.2019.00608"},{"issue":"11","key":"10.3233\/IDA-230150_ref28","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1109\/TPAMI.2012.21","article-title":"Scalable active learning for multiclass image classification","volume":"34","author":"Joshi","year":"2012","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.3233\/IDA-230150_ref29","doi-asserted-by":"crossref","unstructured":"D.D. Lewis and J. Catlett, Heterogeneous uncertainty sampling for supervised learning, in: Machine Learning Proceedings 1994, Elsevier, 1994, pp.\u00a0148\u2013156.","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"10.3233\/IDA-230150_ref30","doi-asserted-by":"crossref","unstructured":"M.-F. Balcan, A. Broder and T. Zhang, Margin based active learning, in: Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA; June 13\u201315, 2007. Proceedings 20, Springer, 2007, pp.\u00a035\u201350.","DOI":"10.1007\/978-3-540-72927-3_5"},{"key":"10.3233\/IDA-230150_ref31","doi-asserted-by":"crossref","unstructured":"P. Donmez, J.G. Carbonell and P.N. Bennett, Dual strategy active learning, in: Machine Learning: ECML 2007: 18th European Conference on Machine Learning, Warsaw, Poland, September 17\u201321, 2007. Proceedings 18, Springer, 2007, pp.\u00a0116\u2013127.","DOI":"10.1007\/978-3-540-74958-5_14"},{"issue":"3","key":"10.3233\/IDA-230150_ref32","doi-asserted-by":"crossref","first-page":"607","DOI":"10.3233\/IDA-194608","article-title":"An active learning ensemble method for regression tasks","volume":"24","author":"Fazakis","year":"2020","journal-title":"Intelligent Data Analysis"},{"key":"10.3233\/IDA-230150_ref33","doi-asserted-by":"crossref","unstructured":"H.S. Seung, M. Opper and H. Sompolinsky, Query by committee, in: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 1992, pp.\u00a0287\u2013294.","DOI":"10.1145\/130385.130417"},{"key":"10.3233\/IDA-230150_ref34","doi-asserted-by":"crossref","unstructured":"Z. Xu, K. Yu, V. Tresp, X. Xu and J. Wang, Representative sampling for text classification using support vector machines, in: Advances in Information Retrieval: 25th European Conference on IR Research, ECIR 2003, Pisa, Italy, April 14\u201316, 2003. Proceedings 25, Springer, 2003, pp.\u00a0393\u2013407.","DOI":"10.1007\/3-540-36618-0_28"},{"key":"10.3233\/IDA-230150_ref35","doi-asserted-by":"crossref","unstructured":"S. Dasgupta and D. Hsu, Hierarchical sampling for active learning, in: Proceedings of the 25th International Conference on Machine Learning, 2008, pp.\u00a0208\u2013215.","DOI":"10.1145\/1390156.1390183"},{"key":"10.3233\/IDA-230150_ref37","doi-asserted-by":"crossref","unstructured":"V. Prabhu, A. Chandrasekaran, K. Saenko and J. Hoffman, Active domain adaptation via clustering uncertainty-weighted embeddings, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp.\u00a08505\u20138514.","DOI":"10.1109\/ICCV48922.2021.00839"},{"key":"10.3233\/IDA-230150_ref38","doi-asserted-by":"crossref","unstructured":"K. Saito, D. Kim, S. Sclaroff, T. Darrell and K. Saenko, Semi-supervised domain adaptation via minimax entropy, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp.\u00a08050\u20138058.","DOI":"10.1109\/ICCV.2019.00814"},{"key":"10.3233\/IDA-230150_ref39","doi-asserted-by":"crossref","unstructured":"M. Xie, Y. Li, Y. Wang, Z. Luo, Z. Gan, Z. Sun, M. Chi, C. Wang and P. Wang, Learning distinctive margin toward active domain adaptation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp.\u00a07993\u20138002.","DOI":"10.1109\/CVPR52688.2022.00783"},{"key":"10.3233\/IDA-230150_ref40","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume":"33","author":"Xie","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.3233\/IDA-230150_ref41","doi-asserted-by":"crossref","unstructured":"H. Venkateswara, J. Eusebio, S. Chakraborty and S. Panchanathan, Deep hashing network for unsupervised domain adaptation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp.\u00a05018\u20135027.","DOI":"10.1109\/CVPR.2017.572"},{"key":"10.3233\/IDA-230150_ref43","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp.\u00a0770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"10.3233\/IDA-230150_ref44","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Communications of the ACM"},{"key":"10.3233\/IDA-230150_ref46","doi-asserted-by":"crossref","unstructured":"E.D. Cubuk, B. Zoph, J. Shlens and Q.V. Le, Randaugment: Practical automated data augmentation with a reduced search space, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp.\u00a0702\u2013703.","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"10.3233\/IDA-230150_ref47","doi-asserted-by":"crossref","unstructured":"I. Dagan and S.P. 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