{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T20:53:32Z","timestamp":1788900812328,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T00:00:00Z","timestamp":1580083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61271115"],"award-info":[{"award-number":["61271115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The transfer learning method is used to extend our existing model to more difficult scenarios, thereby accelerating the training process and improving learning performance. The conditional adversarial domain adaptation method proposed in 2018 is a particular type of transfer learning. It uses the domain discriminator to identify which images the extracted features belong to. The features are obtained from the feature extraction network. The stability of the domain discriminator directly affects the classification accuracy. Here, we propose a new algorithm to improve the predictive accuracy. First, we introduce the Lipschitz constraint condition into domain adaptation. If the constraint condition can be satisfied, the method will be stable. Second, we analyze how to make the gradient satisfy the condition, thereby deducing the modified gradient via the spectrum regularization method. The modified gradient is then used to update the parameter matrix. The proposed method is compared to the ResNet-50, deep adaptation network, domain adversarial neural network, joint adaptation network, and conditional domain adversarial network methods using the datasets that are found in Office-31, ImageCLEF-DA, and Office-Home. The simulations demonstrate that the proposed method has a better performance than other methods with respect to accuracy.<\/jats:p>","DOI":"10.3390\/info11020068","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T11:41:57Z","timestamp":1580125317000},"page":"68","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Spectral Normalization for Domain Adaptation"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9499-1911","authenticated-orcid":false,"given":"Liquan","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control &amp; Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Power System Simulation and Control &amp; Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin 132012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Venkateswara, H., Eusebio, J., Chakraborty, S., and Panchanathan, S. (2017, January 21\u201326). Deep hashing network for unsupervised domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","article-title":"An overview of deep learning in medical imaging focusing on MRI","volume":"29","author":"Lundervold","year":"2019","journal-title":"Z. Med. Phys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","article-title":"Visual domain adaptation: A survey of recent advances","volume":"32","author":"Patel","year":"2015","journal-title":"IEEE Signal Proc. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1109\/TKDE.2016.2551241","article-title":"Incremental evolving domain adaptation","volume":"28","author":"Bitarafan","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1109\/TNNLS.2018.2863240","article-title":"Domain Adaption via Feature Selection on Explicit Feature Map","volume":"30","author":"Deng","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_9","first-page":"3760","article-title":"Distribution-matching embedding for visual domain adaptation","volume":"17","author":"Baktashmotlagh","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Hu, L., Hu, L., Peng, X., and Yu, P.S. (2018, January 19\u201323). Stratified transfer learning for cross-domain activity recognition. Proceedings of the IEEE International Conference on Pervasive Computing and Communications, Athens, Greece.","DOI":"10.1109\/PERCOM.2018.8444572"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Hao, S., Feng, W., and Shen, Z. (2017, January 18\u201321). Balanced distribution adaptation for transfer learning. Proceedings of the IEEE International Conference on Data Mining, New Orleans, LA, USA.","DOI":"10.1109\/ICDM.2017.150"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, B., and Saenko, K. (2016, January 8\u201316). Deep coral: Correlation alignment for deep domain adaptation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref_13","unstructured":"Li, J., Zhao, J., and Lu, K. (2016, January 9\u201316). Joint Feature Selection and Structure Preservation for Domain Adaptation. Proceedings of the International Joint Conferences on Artificial Intelligence, New York, NY, USA."},{"key":"ref_14","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M.I. (2015, January 6\u201311). Learning transferable features with deep adaptation networks. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_15","unstructured":"Long, M., Zhu, H., Wang, J., and Jordan, M.I. (2017, January 7\u20139). Deep transfer learning with joint adaptation networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_16","unstructured":"Long, M., Zhu, H., Wang, J., and Jordan, M.I. (2016, January 5\u201310). Unsupervised domain adaptation with residual transfer networks. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., and Saenko, K. (2015, January 7\u201313). Simultaneous deep transfer across domains and tasks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.463"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.patcog.2018.03.005","article-title":"Adaptive batch normalization for practical domain adaptation","volume":"80","author":"Li","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_19","unstructured":"Ganin, Y., and Lempitsky, V. (2015, January 6\u201311). Unsupervised domain adaptation by backpropagation. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_20","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 3\u20138). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T. (2017, January 21\u201326). Adversarial discriminative domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, HI, USA.","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref_22","unstructured":"Long, M., Cao, Z., Wang, J., and Jordan, M.I. (2018, January 3\u20138). Conditional adversarial domain adaptation. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shen, J., Qu, Y., Zhang, W., and Yu, Y. (2018, January 2\u20137). Wasserstein distance guided representation learning for domain adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"ref_24","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_25","unstructured":"Hoffman, J., Tzeng, E., Park, T., Zhu, J., Isola, P., Saenko, K., Efros, A., and Darrell, T. (, January 10\u201315). CyCADA: Cycle-Consistent Adversarial Domain Adaptation. Proceedings of the 35th International Conference on Machine Learning, Vienna, Austria."},{"key":"ref_26","unstructured":"Liu, M.Y., Breuel, T., and Kautz, J. (2017, January 4\u20139). Unsupervised image-to-image translation networks. Proceedings of the Advances in Neural Information Processing Systems, Long Beach CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cao, Z., Long, M., Wang, J., and Jordan, M.I. (2018, January 18\u201322). Partial transfer learning with selective adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00288"},{"key":"ref_28","first-page":"2128","article-title":"Spectral normalization for generative adversarial networks","volume":"28","author":"Miyato","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Saenko, K., Kulis, B., Fritz, M., and Darrell, T. (2010, January 5\u201311). Adapting visual category models to new domains. Proceedings of the European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref_30","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 10\u201312). PyTorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_31","first-page":"985","article-title":"Covariate shift adaptation by importance weighted cross validation","volume":"8","author":"Sugiyama","year":"2007","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TPAMI.2018.2868685","article-title":"Transferable representation learning with deep adaptation networks","volume":"41","author":"Long","year":"2019","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_33","unstructured":"You, K., Wang, X., Long, M., and Jordan, M.I. (2019, January 16\u201319). Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. Proceedings of the International Conference on Machine Learning, Boca Raton, FL, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10994-009-5152-4","article-title":"A theory of learning from different domains","volume":"79","author":"Blitzer","year":"2010","journal-title":"Mach. Learn."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/68\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:30:33Z","timestamp":1760362233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/2\/68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["info11020068"],"URL":"https:\/\/doi.org\/10.3390\/info11020068","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,27]]}}}