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.
- Mutual Information-guided Knowledge Transfer for Novel Class Discovery [paper]
- Open-world Contrastive Learning [paper]
- Automatically Discovering Novel Visual Categories with Adaptive Prototype Learning [paper]
- A Method for Discovering Novel Classes in Tabular Data [paper]
- A Closer Look at Novel Class Discovery from the Labeled Set [paper]
- Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery [paper]
- A Simple Parametric Classification Baseline for Generalized Category Discovery [paper] [code]
- Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning [paper]
- Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning (EMNLP 2022) [paper]
- Grow and Merge: A Unified Framework for Continuous Categories Discovery (NeurIPS 2022) [paper]
- XCon: Learning with Experts for Fine-grained Category Discovery (BMVC 2022) [paper] [code]
- Novel Class Discovery without Forgetting (ECCV 2022) [paper]
- Class-incremental Novel Class Discovery (ECCV 2022) [paper] [code]
- OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning (ECCV 2022) [paper]
- Residual Tuning: Toward Novel Category Discovery Without Labels (TNNLS 2022) [paper]
- Meta Discovery: Learning to Discover Novel Classes given Very Limited Data (ICLR 2022) [paper] [code]
- 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]
- Novel Class Discovery in Semantic Segmentation (CVPR 2022) [paper] [code]
- Generalized Category Discovery (CVPR 2022) [paper] [code]
- Spacing Loss for Discovering Novel Categories (CVPR Workshop 2022) [paper]
- Open Set Domain Adaptation By Novel Class Discovery (ICME 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]
- 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]
- 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]
- Progressive Self-Supervised Clustering With Novel Category Discovery (TCYB 2021) [paper]
- Automatically Discovering and Learning New Visual Categories with Ranking Statistics (ICLR 2020) [paper] [code] (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!