{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T14:42:11Z","timestamp":1767796931374,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"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":["U22A2047"],"award-info":[{"award-number":["U22A2047"]}],"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":["62371173"],"award-info":[{"award-number":["62371173"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Semi-supervised learning has attracted widespread attention due to its ability to utilize both labeled and unlabeled data, leading to significant progress in recent years. Conventional semi-supervised learning approaches often rely on a strategy that combines weak and strong image-level augmentations and employs pseudo-labeling techniques, where high-confidence predictions are selected as pseudo-labels while low-confidence ones are discarded. However, such methods tend to overlook the useful information contained in low-confidence samples. Moreover, existing augmentation strategies are mostly limited to the image level and lack feature-level perturbations. To address these limitations, this paper proposes a semi-supervised learning method that integrates complementary labels and auxiliary feature perturbations, aiming to extract valuable information from low-confidence samples and expand the scope of feature-space perturbations. Experiments on the standard CIFAR-10 dataset show that under the extreme setting of only 4 labels per class, our method improves accuracy by 2.58% compared to Fixmatch. On the more complex STL-10 dataset, also with only 4 labels per class, the Top-1 accuracy is improved by 2.59%. In addition, systematic ablation studies are conducted to verify the effectiveness of each component.<\/jats:p>","DOI":"10.3390\/a19010056","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T11:46:43Z","timestamp":1767786403000},"page":"56","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unified Complementary Learning with Feature Perturbation for Semi-Supervised Learning"],"prefix":"10.3390","volume":"19","author":[{"given":"Ke","family":"Wang","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3995-6761","authenticated-orcid":false,"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunfei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anke","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"ref_1","unstructured":"Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., and Pfister, T. (2020). A simple semi-supervised learning framework for object detection. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, L., Xiong, N., Pan, X., Yue, X., Wu, P., and Guo, C. (2023). Improved object detection method utilizing yolov7-tiny for unmanned aerial vehicle photographic imagery. Algorithms, 16.","DOI":"10.3390\/a16110520"},{"key":"ref_3","first-page":"107984","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","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_5","first-page":"2215005","article-title":"Medical image fusion based on semisupervised learning and generative adversarial network","volume":"59","author":"Yin","year":"2022","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Miao, J., Chen, C., Liu, F., Wei, H., and Heng, P.-A. (2023, January 1\u20136). Caussl: Causality-inspired semi-supervised learning for medical image segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01959"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111641","DOI":"10.1016\/j.asoc.2024.111641","article-title":"3D medical image segmentation based on semi-supervised learning using deep co-training","volume":"159","author":"Yang","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8934","DOI":"10.1109\/TKDE.2022.3220219","article-title":"A survey on deep semi-supervised learning","volume":"35","author":"Yang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_12","unstructured":"Ouali, Y., Hudelot, C., and Tami, M. (2020). An overview of deep semi-supervised learning. arXiv."},{"key":"ref_13","unstructured":"Laine, S., and Aila, T. (2016). Temporal ensembling for semi-supervised learning. arXiv."},{"key":"ref_14","unstructured":"Tarvainen, A., and Valpola, H. (2017, January 4\u20139). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_15","first-page":"10759","article-title":"Consistency-based semi-supervised learning for object detection","volume":"32","author":"Jeong","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, S., Kim, H., and Chun, D. (2023, January 5\u20138). UCR-SSL: Uncertainty-based consistency regularization for semi-supervised learning. Proceedings of the 2023 International Conference on Electronics, Information, and Communication (ICEIC), Singapore.","DOI":"10.1109\/ICEIC57457.2023.10049938"},{"key":"ref_17","unstructured":"Kim, B., Choo, J., Kwon, Y.-D., Joe, S., Min, S., and Gwon, Y. (2021). Selfmatch: Combining contrastive self-supervision and consistency for semi-supervised learning. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nassar, I., Herath, S., Abbasnejad, E., Buntine, W., and Haffari, G. (2021, January 20\u201325). All labels are not created equal: Enhancing semi-supervision via label grouping and co-training. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00716"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yu, X., Liu, T., Gong, M., and Tao, D. (2018, January 8\u201314). Learning with biased complementary labels. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01246-5_5"},{"key":"ref_20","unstructured":"Xu, Y., Shang, L., Ye, J., Qian, Q., Li, Y.-F., Sun, B., Li, H., and Jin, R. (2021, January 18\u201324). Dash: Semi-supervised learning with dynamic thresholding. Proceedings of the International Conference on Machine Learning, Online."},{"key":"ref_21","first-page":"596","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_22","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume":"33","author":"Xie","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_23","unstructured":"Lee, D.-H. (2013, January 16\u201321). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. Proceedings of the Workshop on Challenges in Representation Learning, ICML, Atlanta, GA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yang, F., Wu, K., Zhang, S., Jiang, G., Liu, Y., Zheng, F., Zhang, W., Wang, C., and Zeng, L. (2022, January 19\u201324). Class-aware contrastive semi-supervised learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01402"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Iscen, A., Tolias, G., Avrithis, Y., and Chum, O. (2019, January 16\u201320). Label propagation for deep semi-supervised learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00521"},{"key":"ref_26","unstructured":"Antoniou, A., Storkey, A., and Edwards, H. (2017). Data augmentation generative adversarial networks. arXiv."},{"key":"ref_27","unstructured":"Rizve, M.N., Duarte, K., Rawat, Y.S., and Shah, M. (2021). In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning. arXiv."},{"key":"ref_28","unstructured":"Chen, J., Shah, V., and Kyrillidis, A. (2020, January 13\u201318). Negative sampling in semi-supervised learning. Proceedings of the International Conference on Machine Learning, Online."},{"key":"ref_29","first-page":"5644","article-title":"Learning from complementary labels","volume":"30","author":"Ishida","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","unstructured":"Ishida, T., Niu, G., Menon, A., and Sugiyama, M. (2019, January 9\u201315). Complementary-label learning for arbitrary losses and models. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_31","unstructured":"Kim, Y., Yim, J., Yun, J., and Kim, J. (November, January 27). Nlnl: Negative learning for noisy labels. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"128645","DOI":"10.1016\/j.neucom.2024.128645","article-title":"A comprehensive survey on contrastive learning","volume":"610","author":"Hu","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zheng, M., You, S., Huang, L., Wang, F., Qian, C., and Xu, C. (2022, January 18\u201324). SimMatch: Semi-supervised Learning with Similarity Matching. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01407"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","article-title":"Contrastive representation learning: A framework and review","volume":"8","author":"Healy","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, J., Xiong, C., and Hoi, S.C. (2021, January 11\u201317). Comatch: Semi-supervised learning with contrastive graph regularization. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.00934"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1109\/TMM.2022.3158069","article-title":"Semi-Supervised Contrastive Learning With Similarity Co-Calibration","volume":"25","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_37","unstructured":"Wu, Z., and Cui, J. (2024, January 3\u20139). AllMatch: Exploiting all unlabeled data for semi-supervised learning. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, Jeju, Republic of Korea."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8441","DOI":"10.1109\/TNNLS.2022.3228380","article-title":"MutexMatch: Semi-Supervised Learning With Mutex-Based Consistency Regularization","volume":"35","author":"Duan","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_39","first-page":"18408","article-title":"Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","unstructured":"Wang, Y., Chen, H., Heng, Q., Hou, W., Fan, Y., Wu, Z., Wang, J., Savvides, M., Shinozaki, T., and Raj, B. (2022). Freematch: Self-adaptive thresholding for semi-supervised learning. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., and Le, Q.V. (2020, January 14\u201319). Randaugment: Practical automated data augmentation with a reduced search space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref_42","unstructured":"Krizhevsky, A., and Hinton, G. (2009). Learning Multiple Layers of Features from Tiny Images, University of Toronto."},{"key":"ref_43","unstructured":"Coates, A., Ng, A., and Lee, H. (2011, January 11\u201313). An analysis of single-layer networks in unsupervised feature learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_44","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A.Y. (2011, January 16). Reading digits in natural images with unsupervised feature learning. Proceedings of the NIPS Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2016). Wide residual networks. arXiv.","DOI":"10.5244\/C.30.87"},{"key":"ref_46","first-page":"5049","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume":"32","author":"Berthelot","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","unstructured":"Berthelot, D., Carlini, N., Cubuk, E.D., Kurakin, A., Sohn, K., Zhang, H., and Raffel, C. (2019). Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.neunet.2023.11.052","article-title":"Boosting semi-supervised learning with Contrastive Complementary Labeling","volume":"170","author":"Deng","year":"2024","journal-title":"Neural Netw."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/1\/56\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T11:49:37Z","timestamp":1767786577000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/1\/56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,7]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["a19010056"],"URL":"https:\/\/doi.org\/10.3390\/a19010056","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,7]]}}}