# Entropic Open-set Active Learning

arXiv: https://arxiv.org/abs/2312.14126 · HTML: https://mlsanity.com/paper/2312.14126/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 47 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Generative OpenMax for Multi-Class Open Set Classification](https://mlsanity.com/paper/1707.07418/) (2017)
- [Classification-Reconstruction Learning for Open-Set Recognition](https://mlsanity.com/paper/1812.04246/) (2018)
- [C2AE: Class Conditioned Auto-Encoder for Open-set Recognition](https://mlsanity.com/paper/1904.01198/) (2019)
- [Learning Open Set Network with Discriminative Reciprocal Points](https://mlsanity.com/paper/2011.00178/) (2020)
- [Conditional Gaussian Distribution Learning for Open Set Recognition](https://mlsanity.com/paper/2003.08823/) (2020)
- [Efficient Parameter-free Clustering Using First Neighbor Relations](https://mlsanity.com/paper/1902.11266/) (2019)
- [OVANet: One-vs-All Network for Universal Domain Adaptation](https://mlsanity.com/paper/2104.03344/) (2021)
- [SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios](https://mlsanity.com/paper/2107.00717/) (2021)
- [Bayesian Generative Active Deep Learning](https://mlsanity.com/paper/1904.11643/) (2019)
- [Towards Online Domain Adaptive Object Detection](https://mlsanity.com/paper/2204.05289/) (2022)
- [Active Learning for Open-set Annotation](https://mlsanity.com/paper/2201.06758/) (2022)
- [P-ODN: Prototype based Open Deep Network for Open Set Recognition](https://mlsanity.com/paper/1905.01851/) (2019)
- [PMAL: Open Set Recognition via Robust Prototype Mining](https://mlsanity.com/paper/2203.08569/) (2022)
- [Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning](https://mlsanity.com/paper/2210.07805/) (2022)
- [Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations](https://mlsanity.com/paper/2303.16891/) (2023)
- [Active Learning by Query by Committee with Robust Divergences](https://mlsanity.com/paper/2211.10013/) (2022)
- [Unified Classification and Rejection: A One-versus-All Framework](https://mlsanity.com/paper/2311.13355/) (2023)

## Built on by (most cited first, 23 of 23)

- [ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection](https://mlsanity.com/paper/2402.03235/) (2024)
- [Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting](https://mlsanity.com/paper/2404.17245/) (2024)
- [Active Learning for Vision-Language Models](https://mlsanity.com/paper/2410.22187/) (2024)
- [Towards Robust Multimodal Open-set Test-time Adaptation via Adaptive Entropy-aware Optimization](https://mlsanity.com/paper/2501.13924/) (2025)
- [Filter Images First, Generate Instructions Later: Pre-Instruction Data Selection for Visual Instruction Tuning](https://mlsanity.com/paper/2503.07591/) (2025)
- [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)
- [Deep Active Learning in the Open World](https://mlsanity.com/paper/2411.06353/) (2024)
- [Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based Approach](https://mlsanity.com/paper/2502.19691/) (2025)
- [FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information](https://mlsanity.com/paper/2411.05752/) (2024)
- [Certainty and Uncertainty Guided Active Domain Adaptation](https://mlsanity.com/paper/2505.19421/) (2025)
- [Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model](https://mlsanity.com/paper/2408.04917/) (2024)
- [LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model Training](https://mlsanity.com/paper/2411.15217/) (2024)
- [A2LC: Active and Automated Label Correction for Semantic Segmentation](https://mlsanity.com/paper/2506.11599/) (2025)
- [StepAL: Step-aware Active Learning for Cataract Surgical Videos](https://mlsanity.com/paper/2507.22059/) (2025)
- [Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning](https://mlsanity.com/paper/2604.12303/) (2026)
- [SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning](https://mlsanity.com/paper/2512.13635/) (2025)
- [Revisiting Unknowns: Towards Effective and Efficient Open-Set Active Learning](https://mlsanity.com/paper/2603.07898/) (2026)
- [Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification](https://mlsanity.com/paper/2509.24181/) (2025)
- [SAMOSA: Sharpness Aware Minimization for Open Set Active learning](https://mlsanity.com/paper/2510.16757/) (2025)
- [Energy-Based Open-Set Active Learning for Object Classification](https://mlsanity.com/paper/2604.20083/) (2026)
- [FIDAL: Diversity-Aware Federated Active Learning Under Real-World Distribution Shifts](https://mlsanity.com/paper/2609.31637/) (2026)

## Abstract (arXiv)

Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data. Despite impressive performance in closed-set settings, most AL methods fail in real-world scenarios where the unlabeled data contains unknown categories. Recently, a few studies have attempted to tackle the AL problem for the open-set setting. However, these methods focus more on selecting known samples and do not efficiently utilize unknown samples obtained during AL rounds. In this work, we propose an Entropic Open-set AL (EOAL) framework which leverages both known and unknown distributions effectively to select informative samples during AL rounds. Specifically, our approach employs two different entropy scores. One measures the uncertainty of a sample with respect to the known-class distributions. The other measures the uncertainty of the sample with respect to the unknown-class distributions. By utilizing these two entropy scores we effectively separate the known and unknown samples from the unlabeled data resulting in better sampling. Through extensive experiments, we show that the proposed method outperforms existing state-of-the-art methods on CIFAR-10, CIFAR-100, and TinyImageNet datasets. Code is available at urlhttps://github.com/bardisafa/EOAL.