Self Supervised Learning for Image Classification With Few Labels
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Abstract
Labeled data are often limited in image classification because annotation is costly and slow. This study proposes a self-supervised learning method for image classification with few labels. The method uses multi-view data augmentation, SimCLR and MoCo pretraining, and pseudo-label fine-tuning to learn useful features from unlabeled images. Experiments were conducted on CIFAR-100 and STL-10 with only 10% labeled data. Compared with supervised training under the same label setting, the proposed method improved classification accuracy by about 9.7%. The accuracy gap from full-supervised training was less than 2.5%, and normalized mutual information increased by about 13%. These results show that contrastive pretraining and pseudo-label learning can reduce the effect of label shortage and improve feature clustering. The method provides a practical way to improve image classification when labeled samples are limited.
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References
Wang, Y., Yin, X., Chen, J., & Wang, Y. (2026). Evidence-Based Study on Low-Burden Digital Phenotyping for Precision Screening of Oral Anti-Obesity Drug Efficacy.
Shoaib, M. A., Ali, R., Bazai, S. U., & Mir, T. (2025). Deep learning techniques for image segmentation and data annotation. In Modern Intelligent Techniques for Image Processing (pp. 63-94). IGI Global Scientific Publishing.
Yan, B. (2026). A Comparative Study: Greedy versus GAN-Based Compressed Sensing for Fast MRI Reconstruction. International Journal of Advance in Applied Science Research, 5(3), 36-42.
Uelwer, T., Robine, J., Wagner, S. S., Höftmann, M., Upschulte, E., Konietzny, S., ... & Harmeling, S. (2025). A survey on self-supervised methods for visual representation learning. Machine Learning, 114(4), 111.
Jiao, Y., Wang, A., Zhao, B., & Shi, T. (2026). Quantitative Study on the Construction and Application Effectiveness of Graffiti Wall Painting Teaching Models in Public Space Contexts.
Kanagarajan, M., Natarajan, M., Rajendran, S., Velusamy, P., Ganesan, S. K., Bose, M., ... & Stephen Inbaraj, B. (2025). AIM-Net: A Resource-Efficient Self-Supervised Learning Model for Automated Red Spider Mite Severity Classification in Tea Cultivation. AgriEngineering, 7(8), 247.
Liu, S., Liu, X., & Feng, H. (2025, November). Research on AI-Driven Visual Design and Immersive Interactive Experiences Based on Multimodal Cognition and User. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 734-740).
Altaheri, H., Karray, F., Islam, M. M., Raju, S. M., & Karimi, A. H. (2025). Bridging Brain with Foundation Models through Self-Supervised Learning. arXiv preprint arXiv:2506.16009.
Chen, F., Li, S., Liang, H., Xu, P., & Yue, L. (2025). Optimization Study of Thermal Management of Domestic SiC Power Semiconductor Based on Improved Genetic Algorithm.
Deorari, M. C., Rajput, I. S., Kumar, A., & Tyagi, S. (2026, February). Self-Supervised Learning for Small Datasets Using Data Augmentation and Contrastive Learning. In 2026 1st International Conference on Advancing Sustainable Solutions through Technologies (ICASST) (pp. 1-6). IEEE.
Liang, R., Feifan, F. N. U., Liang, Y., & Ye, Z. (2025). Emotion-Aware Interface Adaptation in Mobile Applications Based on Color Psychology and Multimodal User State Recognition. Frontiers in Artificial Intelligence Research, 2(1), 51-57.
Petti, D., Li, C., & Liu, N. (2026). Contrastive multi-view representation learning for multi-camera plant phenotyping: A cotton field study. Plant Phenomics, 100193.
Zhu, W., Yao, Y., & Yang, J. (2025). Optimizing Financial Risk Control for Multinational Projects: A Joint Framework Based on CVaR-Robust Optimization and Panel Quantile Regression.
Jalal, M. E., & Elmaghraby, A. (2025). Towards Deep Semi-Supervised Continual Learning. IEEE Access.
Gao, X., Chen, J., Huang, M., & Fang, S. (2025). Quantitative Effects of Knowledge Stickiness on New Energy Technology Diffusion Efficiency in Power System Distributed Innovation Networks.
Pacal, I., & Işık, G. (2025). Utilizing convolutional neural networks and vision transformers for precise corn leaf disease identification. Neural Computing and Applications, 37(4), 2479-2496.
Feng, H. (2024, October). Design of Intelligent Charging System for Portable Electronic Devices Based on Internet of Things (IoT). In 2024 5th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) (pp. 568-571). IEEE.
Ghamsarian, N., Nasirihaghighi, S., Schoeffmann, K., & Sznitman, R. (2025). Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation. arXiv preprint arXiv:2505.07691.
Liu, S., & Yim, J. (2025). Research on Generative AI Creation Systems Based on Visual Language Modeling: Human-Machine Collaboration and Cognitive Feedback Mechanisms. Available at SSRN 6139770.
Abreu, A. J. P. O. E. (2025). Overcoming scarce annotations through deep semi-supervised learning in cancer characterisation (Master's thesis, Universidade do Porto (Portugal)).
Gao, G., Gao, R., Gao, R., & Zhou, H. (2026). Engineering Analysis and Quantitative Research on the Platform-Based Evolution of Enterprise Communication Systems.
Schienbein, P., & Blumberger, J. (2025). Data‐Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water. ChemPhysChem, 26(1), e202400490.
Chen, X., Xiao, H., Zeng, Z., Zhang, S., & Xiao, R. (2025). Fine-Grained Multivariate Time Series Anomaly Detection via Causal Inference. Knowledge-Based Systems, 114765.
Pettorelli, N., Williams, J., Schulte to Bühne, H., & Crowson, M. (2025). Deep learning and satellite remote sensing for biodiversity monitoring and conservation. Remote Sensing in Ecology and Conservation, 11(2), 123-132.
Xu, S., Ma, Q., Liu, H., & Yue, L. (2026). Continuous Reorganization and Performance Preservation of Agent Memory Structure Under Distributed Change Environments.