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Awesome-Realistic-Semi-Supervised-Learning

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An awesome paper list of Semi-Supervised Learning under realistic settings.

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# Awesome-Realistic-Semi-Supervised-Learning
An awesome paper list of **Semi-Supervised Learning (SSL)** under realistic (Class-Imbalanced & Open-Set & Open-World) settings.

If you would like to add literature or have other requests, please contact mengqy19@gmail.com.
We will update the list of papers regularly to keep it up to date. :grin:

------

## Open-Set SSL

- [ CVPR-2026 ] Bypassing the Transport Plan: Dynamic Reweighting for Out-of-Distribution Detection with Optimal Transport [[paper](https://openaccess.thecvf.com/content/CVPR2026/papers/Xiao_Bypassing_the_Transport_Plan_Dynamic_Reweighting_for_Out-of-Distribution_Detection_with_CVPR_2026_paper.pdf)]
- [ CVPR-2026 ] PAF: Perturbation-Aware Filtering for Open-Set Semi-Supervised Learning [[paper](https://jiangqy.github.io/publications/CVPR2026_PAF.pdf)] [[code](https://github.com/njustkmg/CVPR2026-PAF)]
- [ AAAI-2026 ] Let the void be void: Robust open-set semi-supervised learning via selective non-alignment [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/39194)]
- [ IJCV-2025 ] Knowledge Distillation Meets Open-Set Semi-supervised Learning [[paper](https://link.springer.com/content/pdf/10.1007/s11263-024-02192-7.pdf)] [[code]([jingyang2017/SRD_ossl](https://github.com/jingyang2017/SRD_ossl))]
- [ TPAMI-2025 ] Towards a Theoretical Understanding of Semi-Supervised Learning under Class Distribution Mismatch [[paper](https://ieeexplore.ieee.org/abstract/document/10904322)] [[code](https://github.com/RUC-DWBI-ML/research/tree/main/WAD-master)]
- [ ICCV-2025 ] CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning [[paper](https://openaccess.thecvf.com/content/ICCV2025/html/Bae_CaliMatch_Adaptive_Calibration_for_Improving_Safe_Semi-supervised_Learning_ICCV_2025_paper.html)]
- [ KDD-2024 ] Asymmetric Beta Loss for Evidence-Based Safe Semi-Supervised Multi-Label Learning [[paper](https://dl.acm.org/doi/abs/10.1145/3637528.3671756)] [[code](https://github.com/hz681/AsymmetricBetaLoss)]
- [ NeurIPS-2024 ] Data Augmentation with Diffusion for Open-Set Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=OP3sNTIE1O)]
- [ ECCV-2024 ] ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07811.pdf)] [[code](https://github.com/walline/prosub)]
- [ ECCV-2024 ] SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/06776.pdf)] [[code](https://github.com/komejisatori/SCOMatch)]
- [ ACL-2024 ] Open-Set Semi-Supervised Text Classification via Adversarial Disagreement Maximization [[paper](https://aclanthology.org/2024.acl-long.118.pdf)] 
- [ ICML-2024 ] InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2403.10658)]
- [ ICML-2024 ] Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning [[paper](https://palm.seu.edu.cn/zhangml/files/ICML'24c.pdf)] [[code](https://palm.seu.edu.cn/zhangml/files/BDMatch.rar)]
- [ IJCAI-2024] Partial Optimal Transport Based Out-of-Distribution Detection for Open-Set Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=3WB5hT27zf)]
- [ WACV-2024 ] Improving Open-Set Semi-Supervised Learning with Self-Supervision [[paper](https://openaccess.thecvf.com/content/WACV2024/papers/Wallin_Improving_Open-Set_Semi-Supervised_Learning_With_Self-Supervision_WACV_2024_paper.pdf)] [[code](https://github.com/walline/ssl-tf2-sefoss)]
- [ AAAI-2024 ] Unknown-Aware Graph Regularization for Robust Semi-supervised Learning from Uncurated Data [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/29227/30315)] [[code](https://github.com/heejokong/UAGreg)]
- [ AAAI-2024 ] ANEDL: Adaptive Negative Evidential Deep Learning for Open-Set Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2303.12091)]
- [ ICCV-2023 ] Rethinking Safe Semi-supervised Learning: Transferring the Open-set Problem to A Close-set One [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Ma_Rethinking_Safe_Semi-supervised_Learning_Transferring_the_Open-set_Problem_to_A_ICCV_2023_paper.pdf)]
- [ ICCV-2023 ] SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Fan_SSB_Simple_but_Strong_Baseline_for_Boosting_Performance_of_Open-Set_ICCV_2023_paper.pdf)] [[code](https://github.com/YUE-FAN/SSB)]
- [ ICCV-2023 ] Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatch [[paper](https://arxiv.org/pdf/2308.11874v1)] [[code](https://github.com/RUC-DWBI-ML/research/tree/main/WAD-master)]
- [ ICCV-2023 ] IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization [[paper](https://arxiv.org/pdf/2308.13168)] [[code](https://github.com/nukezil/IOMatch)]
- [ KDD-2023 ] Open-Set Semi-Supervised Text Classification with Latent Outlier Softening [[paper](https://dl.acm.org/doi/pdf/10.1145/3580305.3599456)] [[code](https://github.com/BDBC-KG-NLP/SIGKDD2023_Latent_Outlier_Softening)]
- [ ICML-2023 ] Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions [[paper](https://openreview.net/pdf?id=dZA7WtCULT)]
- [ CVPR-2023 ] Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learning [[paper](https://aimia-pku.github.io/assets/files/7.-Out-of-Distributed-SemanticPruningforRobustSemi-SupervisedLearning.pdf)] [[code](https://github.com/rain305f/OSP)]
- [ ICLR-2023 ] RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data [[paper](https://openreview.net/pdf?id=G1H4NSATlr)] [[code](https://openreview.net/attachment?id=G1H4NSATlr&name=supplementary_material)]
- [ Arxiv-2023 ] Improving Open-Set Semi-Supervised Learning with Self-Supervision [[paper](https://arxiv.org/pdf/2301.10127)]
- [ TMLR-2023 ] On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning [[paper](https://openreview.net/forum?id=tLG26QxoD8)]
- [ NeurIPS-2022-Workshop ] Semi-supervised Learning from Uncurated Echocardiogram Images with Fix-A-Step [[paper](https://www.michaelchughes.com/papers/HuangSidhomEtAl_MedNeurIPS_2022.pdf)]
- [ AAAI-2022 ] Not All Parameters Should Be Treated Equally: Deep Safe Semi-Supervised Learning under Class Distribution Mismatch [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/20644/20403)] [[code](https://github.com/Zhanlo/SPL)]
- [ CVPR-2022 ] Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class Unlabeled Data [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/He_Safe-Student_for_Safe_Deep_Semi-Supervised_Learning_With_Unseen-Class_Unlabeled_Data_CVPR_2022_paper.pdf)] [[code](https://github.com/Zhanlo/Safe-Student)]
- [ CVPR-2022 ] Class-Aware Contrastive Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2203.02261)] [[code](https://github.com/TencentYoutuResearch/Classification-SemiCLS)]
- [ TMM-2022 ] They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning [[paper](https://ieeexplore.ieee.org/abstract/document/9786767/)] [[code](https://github.com/zhuohuangai/TOOR)]
- [ ICDM-2022 ] How Out-of-Distribution Data Hurts Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2010.03658)]
- [ NeurIPS-2021 ] Universal Semi-Supervised Learning [[paper](https://proceedings.neurips.cc/paper/2021/hash/e06f967fb0d355592be4e7674fa31d26-Abstract.html)] [[code](https://github.com/josephioos/cafa)]
- [ NeurIPS-2021 ] OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization [[paper](https://proceedings.neurips.cc/paper/2021/hash/da11e8cd1811acb79ccf0fd62cd58f86-Abstract.html)] [[code](https://github.com/VisionLearningGroup/OP_Match)]
- [ ICCV-2021 ] Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [[paper](http://openaccess.thecvf.com/content/ICCV2021/html/Huang_Trash_To_Treasure_Harvesting_OOD_Data_With_Cross-Modal_Matching_for_ICCV_2021_paper.html)] [[code](https://github.com/huangjk97/T2T)]
- [ Arxiv-2021 ] An Empirical Study and Analysis on Open-Set Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2101.08237)]
- [ ECCV-2020 ] Multi-task curriculum framework for open-set semi-supervised learning [[paper](https://link.springer.com/chapter/10.1007/978-3-030-58610-2_26)] [[code](https://github.com/YU1ut/Multi-Task-Curriculum-Framework-for-Open-Set-SSL)]
- [ ICML-2020 ] Safe deep semi-supervised learning for unseen-class unlabeled data [[paper](https://proceedings.mlr.press/v119/guo20i.html)] [[code](https://github.com/guolz-ml/DS3L)]
- [ AAAI-2020 ] Semi-Supervised Learning under Class Distribution Mismatch [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/5763/5619)]
- [ NeurIPS-2018 ] Realistic Evaluation of Deep Semi-Supervised Learning Algorithms [[paper](https://proceedings.neurips.cc/paper/2018/file/c1fea270c48e8079d8ddf7d06d26ab52-Paper.pdf)] [[code](https://github.com/brain-research/realistic-ssl-evaluation)]

------

## Open-World SSL

- [ CVPR-2026 ] SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning [[paper](https://openaccess.thecvf.com/content/CVPR2026/papers/Liu_SECOS_Semantic_Capture_for_Rigorous_Classification_in_Open-World_Semi-Supervised_Learning_CVPR_2026_paper.pdf)] [[code](https://github.com/ganchi-huanggua/OSSL-Classification)]
- [ CVPR-2025 ] Learning Textual Prompts for Open-World Semi-Supervised Learning [[paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_Learning_Textual_Prompts_for_Open-World_Semi-Supervised_Learning_CVPR_2025_paper.pdf)] [[code](https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_Learning_Textual_Prompts_CVPR_2025_supplemental.zip)]
- [ NeurIPS-2024 ] OwMatch: Conditional Self-Labeling with Consistency for Open-world Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=rle9X7DQuH)] [[code](https://github.com/niusj03/OwMatch)]
- [ arXiv-2024 ] Towards Realistic Long-tailed Semi-supervised Learning in an Open World [[paper](https://arxiv.org/abs/2405.14516)] [[code](https://github.com/heyuanpengpku/ROLSSL)]
- [ CVPR-2024 ] Targeted Representation Alignment for Open-World Semi-Supervised Learning [[paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_Targeted_Representation_Alignment_for_Open-World_Semi-Supervised_Learning_CVPR_2024_paper.pdf)] [[code](https://github.com/Justherozen/TRAILER)]
- [ IJCAI-2024 ] Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2309.11930.pdf)] [[code](https://github.com/yebo0216best/lps-main)]
- [ AAAI-2024 ] Semi-supervised Open-World Object Detection [[paper](https://arxiv.org/pdf/2402.16013)] [[code](https://github.com/sahalshajim/SS-OWFormer)]
- [ NeurIPS-2023 ] A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2311.03524)] [[code](https://github.com/deeplearning-wisc/sorl)]
- [ NeurIPS-2023 ] Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=zrLxHYvIFL)] [[code](https://github.com/rain305f/TIDA)]
- [ CIKM-2023 ] WOT-Class: Weakly Supervised Open-world Text Classification [[paper](https://arxiv.org/pdf/2305.12401)]
- [ ACL-ARR-2023 ] OW-Class: Open-world Semi-supervised Text Classification [[paper](https://openreview.net/pdf?id=wQxftYkCdE)]
- [ NeurIPS-2022 ] Robust Semi-Supervised Learning when Not All Classes have Labels [[paper](https://openreview.net/pdf?id=lDohSFOHr0)] [[code](https://www.lamda.nju.edu.cn/code_NACH.ashx)]
- [ ICLR-2022 ] Open-World Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=O-r8LOR-CCA)] [[code](https://github.com/snap-stanford/orca)]
- [ ECCV-2022 ] OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning [[paper](https://link.springer.com/chapter/10.1007/978-3-031-19821-2_22)] [[code](https://github.com/nayeemrizve/OpenLDN)]
- [ ECCV-2022 ] Towards Realistic Semi-Supervised Learning [[paper](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136910423.pdf)] [[code](https://github.com/nayeemrizve/TRSSL)]

------

## Class-Imbalanced SSL

- [ AAAI-2026 ] Sampling Control for Imbalanced Calibration in Semi-Supervised Learning [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/39791/43752)] [[code](https://github.com/Sheldon04/SC-SSL)]

- [ ICML-2026 ] Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2605.07557)] [[code](https://github.com/Yaxin-ML/SAGE)]

- [ ICLR-2026 ] Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=e15SYMcsTs)] [[code](https://jiajun0425.github.io/DyTrim/)]

- [ ICLR-2026 ] SCAD: Super-Class-Aware Debiasing for Long-Tailed Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=aSCtAZEcRa)] [[code](https://github.com/aitrics-tom/SCAD)]

- [ ICLR-2026 ] CoLA: Co-Calibrated Logit Adjustment for Long-Tailed Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=pI9n8wAR80)]

- [ ICML-2025 ] A Square Peg in a Square Hole: Meta-Expert for Long-Tailed Semi-Supervised Learning [[paper](https://openreview.net/pdf?id=h0ZeiDRN8A)] [[code](https://github.com/yaxinhou/Meta-Expert)]

- [ ICCV-2025 ] Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch [[paper](https://openaccess.thecvf.com/content/ICCV2025/papers/Lee_Learnable_Logit_Adjustment_for_Imbalanced_Semi-Supervised_Learning_under_Class_Distribution_ICCV_2025_paper.pdf)]

- [ NeurIPS-2025 ] Keep It on a Leash: Controllable Pseudo-label Generation Towards Realistic Long-Tailed Semi-Supervised Learning [[paper](https://proceedings.neurips.cc/paper_files/paper/2025/file/abcd225747ec4a176a5ff59e56e0d2eb-Paper-Conference.pdf)] [[code](https://github.com/yaxinhou/CPG)]

- [ AAAI-2025 ] LCGC Learning from Consistency Gradient Conflicting for Class-Imbalanced Semi-Supervised Debiasing [[paper](https://ojs.aaai.org/index.php/AAAI/article/view/35474)] [[code](https://github.com/Cyue0316/LCGC)]

- [ NeurIPS-2024 ] Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition [[paper](https://arxiv.org/pdf/2410.06109)] [[code](https://github.com/zhouzihao11/CCL)]

- [ ECCV-2024 ] Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibration [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/07132.pdf)] [[code](https://github.com/emasa/ADELLO-LTSSL)]

- [ ECCV-2024 ] Rebalancing Using Estimated Class Distribution for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch [[paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/03287.pdf)] [[code](https://github.com/taemin-park/RECD)]

- [ ICML-2024 ] SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2402.13505v1)] [[code](https://github.com/LeapLabTHU/SimPro)]

- [ CVPR-2024 ] CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2403.10391v1)] [[code](https://github.com/LeeHyuck/CDMAD)]

- [ CVPR-2024 ] BEM: Balanced and Entropy-based Mix for Long-Tailed Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2404.01179)]

- [ TMM-2024 ] DCRP: Class-Aware Feature Diffusion Constraint and Reliable Pseudo-Labeling for Imbalanced Semi-Supervised Learning [[paper](https://ieeexplore.ieee.org/document/10417792)] [[code](https://github.com/guoxiaoyuatbjtu/DCRP)]

- [ MachineLearning-2024 ] Transfer and share: semi-supervised learning from long-tailed data [[paper](https://link.springer.com/article/10.1007/s10994-022-06247-z)] [[code](https://github.com/Stomach-ache/TRAS)]

- [ AAAI-2024 ] Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning [[paper](https://arxiv.org/pdf/2312.15702)] [[code](https://github.com/machengcheng2016/CPE-LTSSL)]

- [ AAAI-2024 ] BaCon: Boosting Imbalanced Semi-Supervised Learning via Balanced Feature-Level Contrastive Learning [[paper](https://www.cis.pku.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1887772040&wbfileid=14433382)]
  
- [ AAAI-2024 ] Twice Class Bias Correction for Imbalanced Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2312.16604v1)] [[code](https://github.com/Lain810/TCBC)]

- [ TMLR-2023 ] Novel class discovery for long-tailed recognition [[paper](https://openreview.net/forum?id=ey5b7kODvK)] [[code](https://github.com/kleinzcy/NCDLR)]

- [ ICCV-2023 ] Towards Semi-supervised Learning with Non-random Missing Labels [[paper](https://arxiv.org/pdf/2308.08872v1)] [[code](https://github.com/NJUyued/PRG4SSL-MNAR)]

- [ CVPR-2023 ] Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency Is All You Need [[paper](http://palm.seu.edu.cn/weit/paper/CVPR2023_ACR.pdf)] [[code](https://github.com/Gank0078/ACR)]

- [ ICLR-2023 ] InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning [[paper](https://openreview.net/pdf?id=m6ahb1mpwwX)] [[code](https://github.com/WisconsinAIVision/INPL)]

- [ ICLR-2023 ] Imbalanced Semi-supervised Learning with Bias Adaptive Classifier [[paper](https://openreview.net/pdf?id=rVM8wD2G7Dy)] [[code](https://github.com/renzhenwang/bias-adaptive-classifier)]

- [ ACL-2023 ] Prototype-Guided Pseudo Labeling for Semi-Supervised Text Classification [[paper](https://aclanthology.org/2023.acl-long.904.pdf)]

- [ WACV-2023 ] Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning [[paper](https://openaccess.thecvf.com/content/WACV2023/papers/Lazarow_Unifying_Distribution_Alignment_as_a_Loss_for_Imbalanced_Semi-Supervised_Learning_WACV_2023_paper.pdf)]

- [ WACV-2023 ] Dynamic Re-weighting for Long-tailed Semi-supervised Learning [[paper](https://openaccess.thecvf.com/content/WACV2023/papers/Peng_Dynamic_Re-Weighting_for_Long-Tailed_Semi-Supervised_Learning_WACV_2023_paper.pdf)]

- [ 2023 ] Towards Semi-Supervised Learning with Non-Random Missing Labels [[paper](https://openreview.net/pdf?id=aibmXGQJPs0)]

- [ ICLR-2022 ] On Non-Random Missing Labels in Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2206.14923)] [[code](https://github.com/JoyHuYY1412/Class_Imbalanced_Semi_Supervised_Learning)]

- [ ICML-2022 ] Smoothed Adaptive Weighting for Imbalanced SSL: Improve Reliability Against Unknown Distribution Data [[paper](https://proceedings.mlr.press/v162/lai22b/lai22b.pdf)] [[code](https://github.com/ZJUJeffLai/SAW_SSL)]

- [ ICML-2022 ] Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding [[paper](https://proceedings.mlr.press/v162/guo22e/guo22e.pdf)] [[code](https://github.com/guolz-ml/Class-Imbalanced-SSL)]

- [ ECCV-2022 ] RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning [[paper](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136900527.pdf)] [[code](https://github.com/NJUyued/RDA4RobustSSL)]

- [ CVPR-2022-Workshop ] SaR: Self-adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-supervised Learning [[paper](https://openaccess.thecvf.com/content/CVPR2022W/L3D-IVU/papers/Lai_SaR_Self-Adaptive_Refinement_on_Pseudo_Labels_for_Multiclass-Imbalanced_Semi-Supervised_Learning_CVPRW_2022_paper.pdf)]

- [ CVPR-2022 ] DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced SSL [[paper](https://arxiv.org/abs/2106.05682)] [[code](https://github.com/ytaek-oh/daso)]

- [ CVPR-2022 ] Debiased Learning from Naturally Imbalanced Pseudo-Labels [[paper](https://arxiv.org/abs/2201.01490)] [[code](https://github.com/frank-xwang/debiased-pseudo-labeling)]

- [ CVPR-2022 ] CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning [[paper](https://arxiv.org/abs/2112.04564)] [[code](https://github.com/YUE-FAN/CoSSL)]

- [ CVPR-2022 ] DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised Learning [[paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_DC-SSL_Addressing_Mismatched_Class_Distribution_in_Semi-Supervised_Learning_CVPR_2022_paper.pdf)]

- [ Arxiv-2022 ] An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning [[paper](https://arxiv.org/abs/2211.11086)]

- [ Arxiv-2022 ] Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision [[paper](https://arxiv.org/pdf/2209.02459)]

- [ NeurIPS-2021 ] ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning [[paper](https://arxiv.org/pdf/2110.10368)] [[code](https://github.com/LeeHyuck/ABC)]

- [ Arxiv-2021 ] Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2106.00209)]

- [ CVPR-2021 ] CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning [[paper](https://arxiv.org/pdf/2102.09559)] [[code](https://github.com/google-research/crest)]

- [ IJCAI-2021 ] Positive-Unlabeled Learning from Imbalanced Data [[paper](https://www.ijcai.org/proceedings/2021/0412.pdf)]

- [ NeurIPS-2020 ] Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning [[paper](https://arxiv.org/pdf/2007.08844)] [[code](https://github.com/bbuing9/DARP)]

- [ NeurIPS-2020 ] Rethinking the Value of Labels for Improving Class-Imbalanced Learning [[paper](https://arxiv.org/pdf/2006.07529)]

------

## Novel Class Discovery

- [ NeurIPS-2024 ] Happy: A Debiased Learning Framework for Continual Generalized Category Discovery [[paper](https://openreview.net/pdf?id=hdUCZiMkFO)] [[code](https://github.com/mashijie1028/Happy-CGCD)]

- [ NeurIPS-2024 ] Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery [[paper](https://openreview.net/pdf?id=seYXqfGT0q)] [[code](https://github.com/HaiyangZheng/PHE)]

- [ NeurIPS-2024 ] Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery [[paper](https://openreview.net/pdf?id=C4NbtYnyQg)] [[code](https://openreview.net/attachment?id=C4NbtYnyQg&name=supplementary_material)]

- [ ICCV-2023 ] Class-relation Knowledge Distillation for Novel Class Discovery [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Gu_Class-relation_Knowledge_Distillation_for_Novel_Class_Discovery_ICCV_2023_paper.pdf)]

- [ ICCV-2023 ] Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery [[paper](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_Learning_Semi-supervised_Gaussian_Mixture_Models_for_Generalized_Category_Discovery_ICCV_2023_paper.pdf)] [[code](https://github.com/DTennant/GPC)]

- [ CVPR-2023 ] PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery [[paper](https://arxiv.org/pdf/2212.05590)] [[code](https://github.com/sheng-eatamath/PromptCAL)]

- [ CVPR-2023 ] Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery [[paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.pdf)] [[code](https://github.com/muliyangm)]

- [ CVPR-2023 ] Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery [[paper](https://arxiv.org/pdf/2210.03591)] [[code](https://github.com/FanZhichen/NCD-IIC)]

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