# DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discovery

arXiv: https://arxiv.org/abs/2504.04804 · HTML: https://mlsanity.com/paper/2504.04804/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 76 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 30 of 63)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Noise or Signal: The Role of Image Backgrounds in Object Recognition](https://mlsanity.com/paper/2006.09994/) (2020)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [An Investigation of Why Overparameterization Exacerbates Spurious Correlations](https://mlsanity.com/paper/2005.04345/) (2020)
- [Masked Siamese Networks for Label-Efficient Learning](https://mlsanity.com/paper/2204.07141/) (2022)
- [SOLO: Segmenting Objects by Locations](https://mlsanity.com/paper/1912.04488/) (2019)
- [Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](https://mlsanity.com/paper/2009.12991/) (2020)
- [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)
- [Learning De-biased Representations with Biased Representations](https://mlsanity.com/paper/1910.02806/) (2019)

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

- [Hyperbolic Category Discovery](https://mlsanity.com/paper/2504.06120/) (2025)
- [SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery](https://mlsanity.com/paper/2510.18740/) (2025)
- [Category Discovery: An Open-World Perspective](https://mlsanity.com/paper/2509.22542/) (2025)
- [SpectralGCD: Spectral Concept Selection and Cross-modal Representation Learning for Generalized Category Discovery](https://mlsanity.com/paper/2602.17395/) (2026)
- [PartCo: Part-Level Correspondence Priors Enhance Category Discovery](https://mlsanity.com/paper/2509.22769/) (2025)
- [Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Experts](https://mlsanity.com/paper/2509.26227/) (2025)
- [Relational Retrieval: Leveraging Known-Novel Interactions for Generalized Category Discovery](https://mlsanity.com/paper/2605.09420/) (2026)
- [Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology](https://mlsanity.com/paper/2510.02760/) (2025)
- [The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery](https://mlsanity.com/paper/2604.14176/) (2026)
- [OmniGCD: Abstracting Generalized Category Discovery for Modality Agnosticism](https://mlsanity.com/paper/2604.14762/) (2026)
- [Generalized Category Discovery under Domain Shifts: From Vision to Vision-Language Models](https://mlsanity.com/paper/2605.00906/) (2026)
- [DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery](https://mlsanity.com/paper/2607.13504/) (2026)
- [Fourier Self-Supervision for Fine-Grained Generalized Category Discovery](https://mlsanity.com/paper/2608.08963/) (2026)
- [CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery](https://mlsanity.com/paper/2608.25692/) (2026)
- [Generalized Biomedicine Discovery](https://mlsanity.com/paper/2610.00120/) (2026)

## Abstract (arXiv)

In this paper, we tackle the problem of Generalized Category Discovery (GCD). Given a dataset containing both labelled and unlabelled images, the objective is to categorize all images in the unlabelled subset, irrespective of whether they are from known or unknown classes. In GCD, an inherent label bias exists between known and unknown classes due to the lack of ground-truth labels for the latter. State-of-the-art methods in GCD leverage parametric classifiers trained through self-distillation with soft labels, leaving the bias issue unattended. Besides, they treat all unlabelled samples uniformly, neglecting variations in certainty levels and resulting in suboptimal learning. Moreover, the explicit identification of semantic distribution shifts between known and unknown classes, a vital aspect for effective GCD, has been neglected. To address these challenges, we introduce DebGCD, a underlineDebiased learning with distribution guidance framework for underlineGCD. Initially, DebGCD co-trains an auxiliary debiased classifier in the same feature space as the GCD classifier, progressively enhancing the GCD features. Moreover, we introduce a semantic distribution detector in a separate feature space to implicitly boost the learning efficacy of GCD. Additionally, we employ a curriculum learning strategy based on semantic distribution certainty to steer the debiased learning at an optimized pace. Thorough evaluations on GCD benchmarks demonstrate the consistent state-of-the-art performance of our framework, highlighting its superiority. Project page: https://visual-ai.github.io/debgcd/