# Dynamic Conceptional Contrastive Learning for Generalized Category Discovery

arXiv: https://arxiv.org/abs/2303.17393 · HTML: https://mlsanity.com/paper/2303.17393/

## Key facts

- Cited by 92 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 30 Mar 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [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)
- [Neighborhood Contrastive Learning for Novel Class Discovery](https://mlsanity.com/paper/2106.10731/) (2021)
- [AutoNovel: Automatically Discovering and Learning Novel Visual Categories](https://mlsanity.com/paper/2106.15252/) (2021)
- [T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval](https://mlsanity.com/paper/2104.10054/) (2021)
- [Efficient Parameter-free Clustering Using First Neighbor Relations](https://mlsanity.com/paper/1902.11266/) (2019)
- [OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World](https://mlsanity.com/paper/2004.05551/) (2020)
- [Weakly Supervised Contrastive Learning](https://mlsanity.com/paper/2110.04770/) (2021)
- [Class-incremental Novel Class Discovery](https://mlsanity.com/paper/2207.08605/) (2022)
- [Novel Class Discovery in Semantic Segmentation](https://mlsanity.com/paper/2112.01900/) (2021)
- [On Learning Contrastive Representations for Learning with Noisy Labels](https://mlsanity.com/paper/2203.01785/) (2022)
- [Deep Clustering by Semantic Contrastive Learning](https://mlsanity.com/paper/2103.02662/) (2021)
- [Slimmable Networks for Contrastive Self-supervised Learning](https://mlsanity.com/paper/2209.15525/) (2022)

## Built on by (most cited first, 30 of 92)

- [Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery](https://mlsanity.com/paper/2305.06144/) (2023)
- [Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery](https://mlsanity.com/paper/2310.19776/) (2023)
- [Contrastive Mean-Shift Learning for Generalized Category Discovery](https://mlsanity.com/paper/2404.09451/) (2024)
- [SelEx: Self-Expertise in Fine-Grained Generalized Category Discovery](https://mlsanity.com/paper/2408.14371/) (2024)
- [Active Generalized Category Discovery](https://mlsanity.com/paper/2403.04272/) (2024)
- [Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery](https://mlsanity.com/paper/2403.07369/) (2024)
- [Federated Generalized Category Discovery](https://mlsanity.com/paper/2305.14107/) (2023)
- [GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery](https://mlsanity.com/paper/2403.09974/) (2024)
- [Solving the Catastrophic Forgetting Problem in Generalized Category Discovery](https://mlsanity.com/paper/2501.05272/) (2025)
- [Democratizing Fine-grained Visual Recognition with Large Language Models](https://mlsanity.com/paper/2401.13837/) (2024)
- [HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts](https://mlsanity.com/paper/2408.04591/) (2024)
- [A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2311.03524/) (2023)
- [Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery](https://mlsanity.com/paper/2410.19213/) (2024)
- [Hyperbolic Category Discovery](https://mlsanity.com/paper/2504.06120/) (2025)
- [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)
- [FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation](https://mlsanity.com/paper/2503.17940/) (2025)
- [DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discovery](https://mlsanity.com/paper/2504.04804/) (2025)
- [Novel Class Discovery for Ultra-Fine-Grained Visual Categorization](https://mlsanity.com/paper/2405.06283/) (2024)
- [VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2409.20146/) (2024)
- [Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement](https://mlsanity.com/paper/2507.06928/) (2025)
- [Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstruction](https://mlsanity.com/paper/2508.10731/) (2025)
- [CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery](https://mlsanity.com/paper/2404.05366/) (2024)
- [Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling](https://mlsanity.com/paper/2403.01053/) (2024)
- [MOS: Modeling Object-Scene Associations in Generalized Category Discovery](https://mlsanity.com/paper/2503.12035/) (2025)
- [Generate, Refine, and Encode: Leveraging Synthesized Novel Samples for On-the-Fly Fine-Grained Category Discovery](https://mlsanity.com/paper/2507.04051/) (2025)
- [FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning](https://mlsanity.com/paper/2403.16561/) (2024)
- [SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discovery](https://mlsanity.com/paper/2510.18740/) (2025)
- [Contextuality Helps Representation Learning for Generalized Category Discovery](https://mlsanity.com/paper/2407.19752/) (2024)
- [Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization](https://mlsanity.com/paper/2506.02334/) (2025)

## Abstract (arXiv)

Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL) framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https://github.com/TPCD/DCCL.