# A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams

arXiv: https://arxiv.org/abs/2106.09170 · HTML: https://mlsanity.com/paper/2106.09170/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 128 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [Continual Unsupervised Representation Learning](https://mlsanity.com/paper/1910.14481/) (2019)
- [Learning by Transduction](https://mlsanity.com/paper/1301.7375/) (2013)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Online Deep Learning: Learning Deep Neural Networks on the Fly](https://mlsanity.com/paper/1711.03705/) (2017)
- [ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning](https://mlsanity.com/paper/2101.00407/) (2021)
- [Multi-Target Prediction: A Unifying View on Problems and Methods](https://mlsanity.com/paper/1809.02352/) (2018)
- [Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments](https://mlsanity.com/paper/1810.07348/) (2018)
- [An Apparent Paradox: A Classifier Trained from a Partially Classified Sample May Have Smaller Expected Error Rate Than That If the Sample Were Completely Classified](https://mlsanity.com/paper/1910.09189/) (2019)
- [Machine Learning (In) Security: A Stream of Problems](https://mlsanity.com/paper/2010.16045/) (2020)
- [Weakly Supervised Deep Learning Approach in Streaming Environments](https://mlsanity.com/paper/1911.00847/) (2019)
- [Probabilistic Regressor Chains with Monte Carlo Methods](https://mlsanity.com/paper/1907.08087/) (2019)

## Built on by (most cited first, 15 of 15)

- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [Learning from Data Streams: An Overview and Update](https://mlsanity.com/paper/2212.14720/) (2022)
- [Label Delay in Online Continual Learning](https://mlsanity.com/paper/2312.00923/) (2023)
- [Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer](https://mlsanity.com/paper/2407.08460/) (2024)
- [Divide-and-Conquer for Enhancing Unlabeled Learning, Stability, and Plasticity in Semi-supervised Continual Learning](https://mlsanity.com/paper/2508.05316/) (2025)
- [Locally Differentially Private Gradient Tracking for Distributed Online Learning over Directed Graphs](https://mlsanity.com/paper/2310.16105/) (2023)
- [Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation](https://mlsanity.com/paper/2211.01315/) (2022)
- [A Systematic Literature Review on Multi-label Data Stream Classification](https://mlsanity.com/paper/2508.17455/) (2025)
- [Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning](https://mlsanity.com/paper/2404.05809/) (2024)
- [A Cyber Manufacturing IoT System for Adaptive Machine Learning Model Deployment by Interactive Causality Enabled Self-Labeling](https://mlsanity.com/paper/2404.05976/) (2024)
- [Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection](https://mlsanity.com/paper/2409.10111/) (2024)
- [Enhancing Classification with Semi-Supervised Deep Learning Using Distance-Based Sample Weights](https://mlsanity.com/paper/2505.14345/) (2025)
- [Open World Autoencoding Drift Detection with Novel Class Recognition in Tabular Non-stationary Data Streams](https://mlsanity.com/paper/2605.29834/) (2026)
- [How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation](https://mlsanity.com/paper/2605.31186/) (2026)
- [DT-GOL: Dual-Track Geometric Online Learning in Nonstationary Environment with Label Delay](https://mlsanity.com/paper/2606.22950/) (2026)

## Abstract (arXiv)

Unlabelled data appear in many domains and are particularly relevant to streaming applications, where even though data is abundant, labelled data is rare. To address the learning problems associated with such data, one can ignore the unlabelled data and focus only on the labelled data (supervised learning); use the labelled data and attempt to leverage the unlabelled data (semi-supervised learning); or assume some labels will be available on request (active learning). The first approach is the simplest, yet the amount of labelled data available will limit the predictive performance. The second relies on finding and exploiting the underlying characteristics of the data distribution. The third depends on an external agent to provide the required labels in a timely fashion. This survey pays special attention to methods that leverage unlabelled data in a semi-supervised setting. We also discuss the delayed labelling issue, which impacts both fully supervised and semi-supervised methods. We propose a unified problem setting, discuss the learning guarantees and existing methods, explain the differences between related problem settings. Finally, we review the current benchmarking practices and propose adaptations to enhance them.