# Robust Semi-Supervised Learning in Open Environments

arXiv: https://arxiv.org/abs/2412.18256 · HTML: https://mlsanity.com/paper/2412.18256/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 60 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 30 of 31)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://mlsanity.com/paper/2103.12051/) (2021)
- [CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2102.09559/) (2021)
- [Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2102.03526/) (2021)
- [Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning](https://mlsanity.com/paper/2007.08844/) (2020)
- [USB: A Unified Semi-supervised Learning Benchmark for Classification](https://mlsanity.com/paper/2208.07204/) (2022)
- [Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning](https://mlsanity.com/paper/2007.11330/) (2020)
- [Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization](https://mlsanity.com/paper/2112.14368/) (2021)
- [How to Leverage Unlabeled Data in Offline Reinforcement Learning](https://mlsanity.com/paper/2202.01741/) (2022)
- [Robustness to Adversarial Perturbations in Learning from Incomplete Data](https://mlsanity.com/paper/1905.13021/) (2019)
- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [Poisoning the Unlabeled Dataset of Semi-Supervised Learning](https://mlsanity.com/paper/2105.01622/) (2021)
- [OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers](https://mlsanity.com/paper/2105.14148/) (2021)
- [Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning](https://mlsanity.com/paper/2108.05617/) (2021)
- [LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model](https://mlsanity.com/paper/2406.04614/) (2024)
- [A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning](https://mlsanity.com/paper/1910.14147/) (2019)
- [S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions](https://mlsanity.com/paper/2305.14095/) (2023)
- [Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories](https://mlsanity.com/paper/2210.06518/) (2022)
- [DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection](https://mlsanity.com/paper/2406.00345/) (2024)
- [Tangent-Normal Adversarial Regularization for Semi-supervised Learning](https://mlsanity.com/paper/1808.06088/) (2018)
- [Transfer and Share: Semi-Supervised Learning from Long-Tailed Data](https://mlsanity.com/paper/2205.13358/) (2022)
- [Distributionally Robust Semi-Supervised Learning for People-Centric Sensing](https://mlsanity.com/paper/1811.05299/) (2018)
- [Offline Imitation Learning with Model-based Reverse Augmentation](https://mlsanity.com/paper/2406.12550/) (2024)
- [LAMDA-SSL: Semi-Supervised Learning in Python](https://mlsanity.com/paper/2208.04610/) (2022)

## Built on by (most cited first, 8 of 8)

- [Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use](https://mlsanity.com/paper/2607.01084/) (2026)
- [Vision-Language Model Selection and Reuse for Downstream Adaptation](https://mlsanity.com/paper/2501.18271/) (2025)
- [Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model](https://mlsanity.com/paper/2503.00884/) (2025)
- [BMIP: Bi-directional Modality Interaction Prompt Learning for VLM](https://mlsanity.com/paper/2501.07769/) (2025)
- [Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning](https://mlsanity.com/paper/2505.19522/) (2025)
- [Weakly-Supervised Contrastive Learning for Imprecise Class Labels](https://mlsanity.com/paper/2505.22028/) (2025)
- [Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning](https://mlsanity.com/paper/2510.10068/) (2025)
- [Bi-CoG: Bi-Consistency-Guided Self-Training for Vision-Language Models](https://mlsanity.com/paper/2510.20477/) (2025)

## Abstract (arXiv)

Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. Conventional SSL studies typically assume close environments where important factors (e.g., label, feature, distribution) between labeled and unlabeled data are consistent. However, more practical tasks involve open environments where important factors between labeled and unlabeled data are inconsistent. It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation, even worse than the simple supervised learning baseline. Manually verifying the quality of unlabeled data is not desirable, therefore, it is important to study robust SSL with inconsistent unlabeled data in open environments. This paper briefly introduces some advances in this line of research, focusing on techniques concerning label, feature, and data distribution inconsistency in SSL, and presents the evaluation benchmarks. Open research problems are also discussed for reference purposes.