# Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning

arXiv: https://arxiv.org/abs/2210.07805 · HTML: https://mlsanity.com/paper/2210.07805/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 13 Oct 2022.

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

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [Likelihood Ratios for Out-of-Distribution Detection](https://mlsanity.com/paper/1906.02845/) (2019)
- [Do Deep Generative Models Know What They Don't Know?](https://mlsanity.com/paper/1810.09136/) (2018)
- [CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances](https://mlsanity.com/paper/2007.08176/) (2020)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [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)
- [Input complexity and out-of-distribution detection with likelihood-based generative models](https://mlsanity.com/paper/1909.11480/) (2019)
- [Contrastive Training for Improved Out-of-Distribution Detection](https://mlsanity.com/paper/2007.05566/) (2020)
- [Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation](https://mlsanity.com/paper/2001.08735/) (2020)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios](https://mlsanity.com/paper/2107.00717/) (2021)
- [NSML: Meet the MLaaS platform with a real-world case study](https://mlsanity.com/paper/1810.09957/) (2018)
- [Open-set Label Noise Can Improve Robustness Against Inherent Label Noise](https://mlsanity.com/paper/2106.10891/) (2021)
- [Weakly Supervised Semantic Segmentation using Out-of-Distribution Data](https://mlsanity.com/paper/2203.03860/) (2022)
- [Removing Undesirable Feature Contributions Using Out-of-Distribution Data](https://mlsanity.com/paper/2101.06639/) (2021)
- [A Survey of Skyline Query Processing](https://mlsanity.com/paper/1704.01788/) (2017)

## Built on by (most cited first, 25 of 25)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Continuous Learning for Android Malware Detection](https://mlsanity.com/paper/2302.04332/) (2023)
- [Active Generalized Category Discovery](https://mlsanity.com/paper/2403.04272/) (2024)
- [Entropic Open-set Active Learning](https://mlsanity.com/paper/2312.14126/) (2023)
- [Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy](https://mlsanity.com/paper/2311.01002/) (2023)
- [LEGO-Learn: Label-Efficient Graph Open-Set Learning](https://mlsanity.com/paper/2410.16386/) (2024)
- [A Unified Approach Towards Active Learning and Out-of-Distribution Detection](https://mlsanity.com/paper/2405.11337/) (2024)
- [Joint Out-of-Distribution Filtering and Data Discovery Active Learning](https://mlsanity.com/paper/2503.02491/) (2025)
- [Inconsistency-Based Data-Centric Active Open-Set Annotation](https://mlsanity.com/paper/2401.04923/) (2024)
- [Active Learning for Continual Learning: Keeping the Past Alive in the Present](https://mlsanity.com/paper/2501.14278/) (2025)
- [Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach](https://mlsanity.com/paper/2502.19691/) (2025)
- [Enhancing Cost Efficiency in Active Learning with Candidate Set Query](https://mlsanity.com/paper/2502.06209/) (2025)
- [Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning](https://mlsanity.com/paper/2307.03741/) (2023)
- [How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning](https://mlsanity.com/paper/2308.08224/) (2023)
- [Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model](https://mlsanity.com/paper/2408.04917/) (2024)
- [AutoAL: Automated Active Learning with Differentiable Query Strategy Search](https://mlsanity.com/paper/2410.13853/) (2024)
- [ALScope: A Unified Toolkit for Deep Active Learning](https://mlsanity.com/paper/2508.04937/) (2025)
- [Revisiting Unknowns: Towards Effective and Efficient Open-Set Active Learning](https://mlsanity.com/paper/2603.07898/) (2026)
- [Prioritizing Informative Features and Examples for Deep Learning from Noisy Data](https://mlsanity.com/paper/2403.00013/) (2024)
- [Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation](https://mlsanity.com/paper/2409.17607/) (2024)
- [SAMOSA: Sharpness Aware Minimization for Open Set Active learning](https://mlsanity.com/paper/2510.16757/) (2025)
- [Beyond Loss Values: Robust Dynamic Pruning via Loss Trajectory Alignment](https://mlsanity.com/paper/2604.07306/) (2026)
- [Energy-Based Open-Set Active Learning for Object Classification](https://mlsanity.com/paper/2604.20083/) (2026)
- [RTLCurator: Label-Efficient Data Curation for RTL Generation](https://mlsanity.com/paper/2607.29283/) (2026)
- [FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts](https://mlsanity.com/paper/2609.31637/) (2026)

## Abstract (arXiv)

Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few active learning studies have attempted to deal with this open-set noise for sample selection by filtering out the noisy examples. However, because focusing on the purity of examples in a query set leads to overlooking the informativeness of the examples, the best balancing of purity and informativeness remains an important question. In this paper, to solve this purity-informativeness dilemma in open-set active learning, we propose a novel Meta-Query-Net,(MQ-Net) that adaptively finds the best balancing between the two factors. Specifically, by leveraging the multi-round property of active learning, we train MQ-Net using a query set without an additional validation set. Furthermore, a clear dominance relationship between unlabeled examples is effectively captured by MQ-Net through a novel skyline regularization. Extensive experiments on multiple open-set active learning scenarios demonstrate that the proposed MQ-Net achieves 20.14% improvement in terms of accuracy, compared with the state-of-the-art methods.