# Active Generalized Category Discovery

arXiv: https://arxiv.org/abs/2403.04272 · HTML: https://mlsanity.com/paper/2403.04272/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 69 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 30 of 44)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Learning to Discover Novel Visual Categories via Deep Transfer Clustering](https://mlsanity.com/paper/1908.09884/) (2019)
- [Generalized Category Discovery](https://mlsanity.com/paper/2201.02609/) (2022)
- [Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2102.03526/) (2021)
- [A Unified Objective for Novel Class Discovery](https://mlsanity.com/paper/2108.08536/) (2021)
- [Automatically Discovering and Learning New Visual Categories with Ranking Statistics](https://mlsanity.com/paper/2002.05714/) (2020)
- [Contextual Diversity for Active Learning](https://mlsanity.com/paper/2008.05723/) (2020)
- [Parametric Classification for Generalized Category Discovery: A Baseline Study](https://mlsanity.com/paper/2211.11727/) (2022)
- [OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World](https://mlsanity.com/paper/2004.05551/) (2020)
- [Dynamic Conceptional Contrastive Learning for Generalized Category Discovery](https://mlsanity.com/paper/2303.17393/) (2023)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery](https://mlsanity.com/paper/2212.05590/) (2022)
- [Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery](https://mlsanity.com/paper/2305.06144/) (2023)
- [Active Learning by Feature Mixing](https://mlsanity.com/paper/2203.07034/) (2022)

## Built on by (most cited first, 30 of 31)

- [ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery](https://mlsanity.com/paper/2504.03755/) (2025)
- [Happy: A Debiased Learning Framework for Continual Generalized Category Discovery](https://mlsanity.com/paper/2410.06535/) (2024)
- [GenHancer: Imperfect Generative Models are Secretly Strong Vision-Centric Enhancers](https://mlsanity.com/paper/2503.19480/) (2025)
- [Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction](https://mlsanity.com/paper/2508.10731/) (2025)
- [MOS: Modeling Object-Scene Associations in Generalized Category Discovery](https://mlsanity.com/paper/2503.12035/) (2025)
- [Aligned Better, Listen Better for Audio-Visual Large Language Models](https://mlsanity.com/paper/2504.02061/) (2025)
- [Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery](https://mlsanity.com/paper/2409.19659/) (2024)
- [Joint Out-of-Distribution Filtering and Data Discovery Active Learning](https://mlsanity.com/paper/2503.02491/) (2025)
- [VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos](https://mlsanity.com/paper/2602.07801/) (2026)
- [Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization](https://mlsanity.com/paper/2506.02334/) (2025)
- [Consistent Supervised-Unsupervised Alignment for Generalized Category Discovery](https://mlsanity.com/paper/2507.04725/) (2025)
- [Deep Active Learning in the Open World](https://mlsanity.com/paper/2411.06353/) (2024)
- [Category Discovery: An Open-World Perspective](https://mlsanity.com/paper/2509.22542/) (2025)
- [Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discovery](https://mlsanity.com/paper/2404.08995/) (2024)
- [Generalized Category Discovery under Domain Shift: A Frequency Domain Perspective](https://mlsanity.com/paper/2511.00573/) (2025)
- [Generalized Category Discovery via Token Manifold Capacity Learning](https://mlsanity.com/paper/2505.14044/) (2025)
- [Uncertainty Quantification in Continual Open-World Learning](https://mlsanity.com/paper/2412.16409/) (2024)
- [Sharpness-aware Dynamic Anchor Selection for Generalized Category Discovery](https://mlsanity.com/paper/2512.12925/) (2025)
- [TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery](https://mlsanity.com/paper/2603.08075/) (2026)
- [Freeze and Cluster: A Simple Baseline for Rehearsal-Free Continual Category Discovery](https://mlsanity.com/paper/2503.09106/) (2025)
- [ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery](https://mlsanity.com/paper/2506.02367/) (2025)
- [Interpretable Text-Guided Image Clustering via Iterative Search](https://mlsanity.com/paper/2506.12514/) (2025)
- [Contrastive Learning with Enhanced Abstract Representations using Grouped Loss of Abstract Semantic Supervision](https://mlsanity.com/paper/2509.12771/) (2025)
- [Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology](https://mlsanity.com/paper/2510.02760/) (2025)
- [Reliable Active Learning from Unreliable Labels via Neural Collapse Geometry](https://mlsanity.com/paper/2510.09740/) (2025)
- [ReLKD: Inter-Class Relation Learning with Knowledge Distillation for Generalized Category Discovery](https://mlsanity.com/paper/2512.07229/) (2025)
- [InfoSculpt: Sculpting the Latent Space for Generalized Category Discovery](https://mlsanity.com/paper/2601.10098/) (2026)
- [Learning through Creation: A Hash-Free Framework for On-the-Fly Category Discovery](https://mlsanity.com/paper/2603.13858/) (2026)
- [Learning Like Humans: Analogical Concept Learning for Generalized Category Discovery](https://mlsanity.com/paper/2603.19918/) (2026)
- [OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism](https://mlsanity.com/paper/2604.14762/) (2026)

## Abstract (arXiv)

Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes. Given that knowledge learned from old classes is not fully transferable to new classes, and that novel categories are fully unlabeled, GCD inherently faces intractable problems, including imbalanced classification performance and inconsistent confidence between old and new classes, especially in the low-labeling regime. Hence, some annotations of new classes are deemed necessary. However, labeling new classes is extremely costly. To address this issue, we take the spirit of active learning and propose a new setting called Active Generalized Category Discovery (AGCD). The goal is to improve the performance of GCD by actively selecting a limited amount of valuable samples for labeling from the oracle. To solve this problem, we devise an adaptive sampling strategy, which jointly considers novelty, informativeness and diversity to adaptively select novel samples with proper uncertainty. However, owing to the varied orderings of label indices caused by the clustering of novel classes, the queried labels are not directly applicable to subsequent training. To overcome this issue, we further propose a stable label mapping algorithm that transforms ground truth labels to the label space of the classifier, thereby ensuring consistent training across different active selection stages. Our method achieves state-of-the-art performance on both generic and fine-grained datasets. Our code is available at https://github.com/mashijie1028/ActiveGCD