Latest Semi-Supervised Learning Research Papers

The newest Semi-Supervised Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Semi-Supervised Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Bidirectional cross-view learning with dynamic region selection for semi-supervised medical image segmentation
  Jiangxiong Fang, Hao Luo, Haihuai Zeng, Jie Jin et al. · Complex & Intelligent Systems · Jul 18, 2026

  Semi-supervised medical image segmentation aims to alleviate the heavy reliance on dense annotations while preserving high segmentation accuracy, yet it remains challenging due to unreliable pseudo-labels and insufficient utilization of unl…

• SSTEAD-net: A semi-supervised temporal encoding and adaptive denoising network for effluent soft sensing in wastewater treatment plants
  Toqeer Ahmed, Awais Khan Jumani, Aftab ul Nabi, Kamlesh et al. · Journal of Water Process En... · Jul 17, 2026
• A semi-supervised tool wear monitoring approach integrating physics-guided weak labels and uncertainty quantification
  Yezhen Peng, Fengwen Yu, Weimin Kang, Nanjie Han et al. · Mechanical Systems and Sign... · Jul 16, 2026
• Explainable Semi-Supervised Learning Framework for Alzheimer’s Disease Prediction Using SHAP-Based Feature Selection and Cost-Sensitive CatBoost
  Abdallah El Chakik, Bilal Nakhal, Ghalia Nassreddine · Sci · Jul 15, 2026

  Alzheimer’s disease (AD) remains a critical global health challenge for which early diagnosis is essential for effective intervention. However, AD detection is a challenging and complex task due to the scarcity of labeled clinical data, cla…

• Distribution-aware probability contrastive learning for class-imbalanced semi-supervised learning
  Pengfei Lv, Jing Chai · OpenAlex · Jul 13, 2026

  Semi-Supervised Learning (SSL) has shown significant advantages by leveraging abundant unlabeled data to enhance model performance with successful applications in computer vision. However, existing SSL methods might exhibit significant perf…

• Semi-supervised ensemble learning with interval type-2 fuzzy-rough sets for Parkinson’s disease prediction from multi-omics
  Ambika Hazarika, Ansuman Kumar, Anindya Halder · Mammalian Genome · Jul 11, 2026
• TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
  Hong Lyu, Mingru Yang, Qianhua He, Yanxiong Li et al. · arXiv · Jul 7, 2026

  There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an $\textbf{A}$u…

• Research on semi-supervised color digital image correlation method for deformation measurement
  Rongli Li, Guo-Qing Han, Xiaoyong Liu, Jia-Ming Hu et al. · Optics & Laser Technology · Jul 6, 2026
• Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data
  Xuanyu Chen, Nan Yang, Shuai Wang, Dong Yuan · arXiv · Jul 2, 2026

  Recent research has introduced distributed self-supervised learning (D-SSL) approaches to leverage vast amounts of unlabeled decentralized data. However, D-SSL faces the critical challenge of data heterogeneity, and there is limited theoret…

• Neuron-Aware Active Few-Shot Learning for LLMs
  Zhuowei Chen, Liwei Chen, Christian Schunn, Raquel Coelho et al. · arXiv · Jul 2, 2026

  Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high perfor…

• C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
  Haoran Jin, Xiting Wang, Shijie Ren, Hong Xie et al. · arXiv · Jun 29, 2026

  Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features, but scaling to large dictionaries exposes fundamental challenges. Systematic studies reveal…

• Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
  Nathan Senyard, Salem Hamdani, Astrid Zhang, Derek Wang et al. · arXiv · Jun 22, 2026

  We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m$^2$ spatial resolution. We combine and harmonize multiple remote sensing data products and…

• Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
  Tianyi Li, Zhiqiang Shen · arXiv · Jun 22, 2026

  Linear mode connectivity (LMC) provides a promising foundation for understanding and merging independently trained neural networks, but existing methods typically optimize the interpolation path from only one model endpoint, limiting their …

• Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks
  Mengyu Zheng, Kai Han, Boxun Li, Haiyang Xu et al. · arXiv · Jun 10, 2026

  General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and pred…

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
  Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan · arXiv (Cornell University) · Jun 3, 2026

  Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep…

• Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
  Rasmus Hannibal Tirsgaard, Laurits Fredsgaard, Marisa Wodrich, Mikkel Jordahn et al. · ICML 2026 regular · Apr 30, 2026

  Machine learning is transforming molecular sciences by accelerating property prediction, simulation, and the discovery of new molecules and materials. Acquiring labeled data in these domains is often costly and time-consuming, whereas large…

• Newton-coupled Dual-Teacher Semi-supervised Learning Framework
  Hongyang He, Xinyuan Song, Yan Zhong, Daizong Liu et al. · ICML 2026 regular · Apr 30, 2026

  Most semi-supervised learning frameworks rely on a single teacher that transfers zero-order supervision through pseudo-labels, constraining the student to imitate categorical outputs without perceiving the loss geometry. This design often l…

• In Context Semi-Supervised Learning
  Jiashuo Fan, Paul Rosu, Aaron T Wang, Lawrence Carin et al. · ICLR 2026 Poster · Jan 26, 2026

  There has been significant recent interest on understanding the capacity of Transformers for in-context learning (ICL), yet most theory focuses on supervised settings with explicitly labeled pairs. In practice, Transformers often perform we…

• Informative missingness and its implications in semi-supervised learning
  Jinran Wu, You‐Gan Wang, Geoffrey J. McLachlan · The Innovation Informatics · Dec 4, 2025

  Semi-supervised learning (SSL) constructs classifiers using both labelled and unlabelled data. It leverages information from labelled samples, whose acquisition is often costly or labour-intensive, together with unlabelled data to enhance p…

• Advanced fault diagnosis in milling cutting tools using vision transformers with semi-supervised learning and uncertainty quantification
  Muhammad Farooq Siddique, Muhammad Umar, Wasim Ahmad, Jong-Myon Kim · Scientific Reports · Nov 27, 2025

  This study proposes a semi-supervised fault diagnosis framework based on vision transformers (ViTs) to enhance the diagnostic accuracy and generalization in machine cutting tools (MCT), particularly under the constraint of limited labeled d…

• Active semi-supervised learning for multi-target regression
  Maira Farias Andrade Lira, Luisa Cavalcante, Celine Vens, Ricardo Prudencio et al. · BNAIC/BeNeLearn 2025 Oral · Oct 15, 2025

  Recent works have proposed the combination of active and semi-supervised learning techniques to efficiently incorporate unlabeled data. The so-called active semi-supervised learning (ASSL) investigates methods to efficiently construct predi…

• Semi-Supervised Contrastive Learning with Orthonormal Prototypes
  Huanran Li, Manh Nguyen, Daniel L. Pimentel-Alarcón · Submitted to ICLR 2026 · Sep 19, 2025

  Contrastive learning has emerged as a powerful method in deep learning, excelling at learning effective representations through contrasting samples from different distributions. However, dimensional collapse, where embeddings converge into …

• Improving Battery Life Prediction With Unlabeled Data: Confidence-Weighted Semi-Supervised Learning With Label Propagation
  Song Zhang, Yannan Li, Jingpeng Tian, Zhihong Man et al. · IEEE Transactions on Transportation Electrification · Apr 1, 2025

  Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for the safety and reliability of electric vehicles (EVs). Although data-driven approaches have been extensively used with high accuracy, they need to…

• UMSCS: A Novel Unpaired Multimodal Image Segmentation Method Via Cross-Modality Generative and Semi-supervised Learning
  Feiyang Yang, Xiongfei Li, Bo Wang, Peihong Teng et al. · International Journal of Computer Vision · Mar 6, 2025
• CGMatch: A Different Perspective of Semi-supervised Learning
  Bo Cheng, Jueqing Lu, Yuan Tian, Haifeng Zhao et al. · Computer Vision and Pattern Recognition · Mar 4, 2025

  Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generalization performance. Recent approaches achieve impressive suc…

• Cognitive workload quantification for air traffic controllers: An ensemble semi-supervised learning approach
  Xiaoqing Yu, Chun-Hsien Chen, Haohan Yang · Advanced Engineering Informatics · Mar 1, 2025
• Dual-view cross attention enhanced semi-supervised learning method for discourse cognitive engagement classification in online course discussions
  Shiqi Liu, Weizheng Kong, Zhi Liu, Jianwen Sun et al. · Expert systems with applications · Mar 1, 2025
• A Unified Framework for Semiparametrically Efficient Semi-Supervised Learning
  Zichun Xu, Daniela M. Witten, A. Shojaie · Semantic Scholar · Feb 25, 2025

  We consider statistical inference under a semi-supervised setting where we have access to both a labeled dataset consisting of pairs $\{X_i, Y_i \}_{i=1}^n$ and an unlabeled dataset $\{ X_i \}_{i=n+1}^{n+N}$. We ask the question: under what…

• Semi-supervised learning for multi-view and non-graph data using Graph Convolutional Networks
  F. Dornaika, J. Bi, J. Charafeddine, H. Xiao · Neural Networks · Feb 1, 2025

  Semi-supervised learning with a graph-based approach has become increasingly popular in machine learning, particularly when dealing with situations where labeling data is a costly process. Graph Convolution Networks (GCNs) have been widely …