Novel Class Discovery (NCD) is a machine learning problem, where novel categories of instances are to be automatically discovered from an unlabelled pool. In contrast to clustering, NCD setting has access to labelled data from a disjoint set of classes. This topic has plausible real-world applications and is gathering much attention in the research community.
Here, we provide a non-exhaustive list of papers that studies NCD.
The OOD-CV workshop hosts a challenge on GCD and OSR at ICCV 2023, the challenge will use the semantic shift benchmark to test GCD performance and OSR performance. Click [here] for more information about the challenge, [here] for more information on the workshop.
- ImbaGCD: Imbalanced Generalized Category Discovery (CVPR2023 workshop) [paper]
- Novel Categories Discovery from probability matrix perspective [paper]
- Federated Generalized Category Discovery [paper]
- CLIP-GCD: Simple Language Guided Generalized Category Discovery [paper]
- What's in a Name? Beyond Class Indices for Image Recognition [paper] (SCD, Semantic Category Discovery)
- NEV-NCD: Negative Learning, Entropy, and Variance regularization based novel action categories discovery [paper] [code]
- Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discovery [paper] [code]
- Novel Class Discovery: an Introduction and Key Concepts [paper]
- Mutual Information-guided Knowledge Transfer for Novel Class Discovery [paper]
- Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning [paper]
- Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier [paper]
- Mutual Information-based Generalized Category Discovery [paper] [code]
- CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery [paper]
- Novel Class Discovery under Unreliable Sampling [paper]
- Novel Class Discovery for Long-tailed Recognition (TMLR 2023) [paper]
- MetaGCD: Learning to Continually Learn in Generalized Category Discovery (ICCV 2023) [paper]
- Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery (ICCV 2023) [paper]
- Class-relation Knowledge Distillation for Novel Class Discovery (ICCV 2023) [paper]
- Incremental Generalized Category Discovery (ICCV 2023) [paper]
- Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery (ICCV 2023) [paper]
- Parametric Classification for Generalized Category Discovery: A Baseline Study (ICCV 2023) [paper] [code]
- An Interactive Interface for Novel Class Discovery in Tabular Data (ECML PKDD 2023, Demo Track) [paper] [code]
- When and How Does Known Class Help Discover Unknown Ones? Provable Understandings Through Spectral Analysis (ICML 2023) [paper] [code]
- NeurNCD: Novel Class Discovery via Implicit Neural Representation (IJCAI 2023) [paper] [code]
- Open-world Semi-supervised Novel Class Discovery (IJCAI 2023) [paper] [code]
- On-the-Fly Category Discovery (CVPR 2023) [paper] [code]
- Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery (CVPR 2023) [paper]
- Dynamic Conceptional Contrastive Learning for Generalized Category Discovery (CVPR 2023) [paper] [code]
- PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery (CVPR 2023) [paper] [code]
- Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery (CVPR 2023) [paper] [code]
- Novel Class Discovery for 3D Point Cloud Semantic Segmentation (CVPR 2023) [paper] [code]
- Generalized Category Discovery with Decoupled Prototypical Network (AAAI 2023) [paper] [code] (DPN)
- Supervised Knowledge May Hurt Novel Class Discovery Performance (TMLR 2023) [paper][code]
- OpenCon: Open-world Contrastive Learning (TMLR 2023) [paper] [code]
- A Method for Discovering Novel Classes in Tabular Data (ICKG 2022) [paper] [code]
- Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning (EMNLP 2022) [paper]
- A Closer Look at Novel Class Discovery from the Labeled Set (NeurIPS Workshop 2022) [paper]
- Grow and Merge: A Unified Framework for Continuous Categories Discovery (NeurIPS 2022) [paper] [code] (GM)
- XCon: Learning with Experts for Fine-grained Category Discovery (BMVC 2022) [paper] [code]
- Novel Class Discovery without Forgetting (ECCV 2022) [paper] (NCDwF)
- Class-incremental Novel Class Discovery (ECCV 2022) [paper] [code] (FRoST)
- OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning (ECCV 2022) [paper] [code]
- Towards Realistic Semi-Supervised Learning (ECCV 2022) [paper] [code]
- Residual Tuning: Toward Novel Category Discovery Without Labels (TNNLS 2022) [paper] [code] (ResTune)
- Open-World Semi-Supervised Learning (ICLR 2022) [paper] [code]
- Meta Discovery: Learning to Discover Novel Classes given Very Limited Data (ICLR 2022) [paper] [code] (MEDI)
- Self-Labeling Framework for Novel Category Discovery over Domains (AAAI 2022) [paper]
- Towards Open-Set Object Detection and Discovery (CVPR Workshop 2022) [paper]
- Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery (CVPR 2022) [paper] [code] (ComEx)
- Novel Class Discovery in Semantic Segmentation (CVPR 2022) [paper] [code]
- Generalized Category Discovery (CVPR 2022) [paper] [code] (GCD)
- Spacing Loss for Discovering Novel Categories (CVPR Workshop 2022) [paper] (Spacing Loss)
- Open Set Domain Adaptation By Novel Class Discovery (ICME 2022) [paper]
- Progressive Self-Supervised Clustering With Novel Category Discovery (TCYB 2022) [paper] [code]
- Novel Class Discovery: A Dependency Approach (ICASSP 2022) [paper]
- Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation (NeurIPS 2021) [paper] [code] (DualRS)
- A Unified Objective for Novel Class Discovery (ICCV 2021) [paper] [code] (UNO)
- Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data (ICCV 2021) [paper] (Joint)
- Neighborhood Contrastive Learning for Novel Class Discovery (CVPR 2021) [paper] [code] (NCL)
- OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World (CVPR 2021) [paper] (OpenMix)
- AutoNovel: Automatically Discovering and Learning Novel Visual Categories (TPAMI 2021) [paper] (AutoNovel aka RS)
- End-to-end novel visual categories learning via auxiliary self-supervision (Neural Networks 2021) [paper]
- Automatically Discovering and Learning New Visual Categories with Ranking Statistics (ICLR 2020) [paper] [code] (AutoNovel aka RS)
- Open-World Class Discovery with Kernel Networks (ICDM 2020) [paper] [code]
- Learning to discover novel visual categories via deep transfer clustering (ICCV 2019) [paper] [code] (DTC)
- Multi-class classification without multi-class labels (ICLR 2019) [paper] [code] (MCL)
Please help us improve the above listing by submitting PRs of other papers in this space. Thank you!