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]

• 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]
• 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]

• 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)

• Automatically Discovering and Learning New Visual Categories with Ranking Statistics (ICLR 2020) [paper] [code] (RS)

• 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)

• Learning to cluster in order to transfer across domains and tasks (ICLR 2018) [paper] [code] (KCL)

• Neural network-based clustering using pairwise constraints (ICLR-workshop 2016) [paper] [code]

Please help us improve the above listing by submitting PRs of other papers in this space. Thank you!