{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T14:40:16Z","timestamp":1777560016572,"version":"3.51.4"},"reference-count":3,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIC"],"published-print":{"date-parts":[[2023,2,9]]},"abstract":"<jats:p>Instance-level contrastive learning such as SimCLR has been successful as a powerful method for representation learning. However, SimCLR suffers from problems of sampling bias, feature bias and model collapse. A set-level based Sampling Enhanced Contrastive Learning (SECL) method based on SimCLR is proposed in this paper. We use the proposed super-sampling method to expand the augmented samples into a contrastive-positive set, which can learn class features of the target sample to reduce the bias. The contrastive-positive set includes Augmentations (the original augmented samples) and Neighbors (the super-sampled samples). We also introduce a samples-correlation strategy to prevent model collapse, where a positive correlation loss or a negative correlation loss is computed to adjust the balance of model\u2019s Alignment and Uniformity. SECL reaches 94.14% classification precision on SST-2 dataset and 89.25% on ARSC dataset. For the multi-class classification task, SECL achieves 90.99% on AGNews dataset. They are all about 1% higher than the precision of SimCLR. Experiments show that the training convergence of SECL is faster, and SECL reduces the risk of bias and model collapse.<\/jats:p>","DOI":"10.3233\/aic-210234","type":"journal-article","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T11:46:17Z","timestamp":1663933577000},"page":"1-12","source":"Crossref","is-referenced-by-count":0,"title":["SECL: Sampling enhanced contrastive learning"],"prefix":"10.1177","volume":"36","author":[{"given":"Yixin","family":"Tang","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiquan","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-210234_ref1","unstructured":"P.\u00a0Bojanowski and A.\u00a0Joulin, Unsupervised learning by predicting noise, in: Proceedings of the International Conference on Machine Learning (ICML), 2017, pp.\u00a01\u201310."},{"key":"10.3233\/AIC-210234_ref3","doi-asserted-by":"crossref","unstructured":"X.\u00a0Chen and K.\u00a0He, Exploring simple Siamese representation learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"10.3233\/AIC-210234_ref19","doi-asserted-by":"crossref","unstructured":"Z.\u00a0Wu, Y.\u00a0Xiong, X.Y.\u00a0Stella and D.\u00a0Lin, Unsupervised feature learning via non-parametric instance discrimination, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.","DOI":"10.1109\/CVPR.2018.00393"}],"container-title":["AI Communications"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/AIC-210234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:28:06Z","timestamp":1777400886000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/AIC-210234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":3,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/aic-210234","relation":{},"ISSN":["1875-8452","0921-7126"],"issn-type":[{"value":"1875-8452","type":"electronic"},{"value":"0921-7126","type":"print"}],"subject":[],"published":{"date-parts":[[2023,2,9]]}}}