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Awesome-Novel-Class-Discovery

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 study NCD.

Some Terms of Problem Setting

  • Novel Class Discovery (NCD, aka Novel Category Discovery)
  • Generalized Category Discovery (GCD, aka Generalized Class Discovery), Open-world Semi-supervised Learning (Open-word SSL)
  • Novel Class Discovery without Forgetting (NCDwF), Class-incremental Novel CLass Discovery (Class-iNCD)
  • Continuous Categories Discovery (CCD)
  • Federated Generalized Category Discovery (Fed-GCD)
  • Active Generalized Category Discovery (Active-GCD)
  • TODO, such as Incremental Generalized Category Discovery (IGCD), Semantic Category Discovery (SCD)

Survey Papers

  • Novel Class Discovery: an Introduction and Key Concepts [paper]
  • Open-world Machine Learning: A Review and New Outlooks [paper]

Preprints

  • HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts [paper]
  • Continual Novel Class Discovery via Feature Enhancement and Adaptation [paper]
  • Exclusive Style Removal for Cross Domain Novel Class Discovery [paper]
  • Revisiting Mutual Information Maximization for Generalized Category Discovery [paper]
  • Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discovery [paper] [code]
  • GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery [paper] [code]
  • Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery [paper]
  • YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery [paper]
  • Beyond the Known: Novel Class Discovery for Open-world Graph Learning [paper]
  • PANDAS: Prototype-based Novel Class Discovery and Detection [paper] [code]
  • Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery [paper]
  • Federated Continual Novel Class Learning [paper]
  • Generalized Category Discovery with Large Language Models in the Loop [paper]
  • Towards Unbiased Training in Federated Open-world Semi-supervised Learning [paper]
  • Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning [paper]
  • Novel class discovery meets foundation models for 3D semantic segmentation [paper]
  • Generalized Category Discovery in Semantic Segmentation [paper] [code]
  • Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery [paper]
  • Generalized Continual Category Discovery [paper]
  • OpenGCD: Assisting Open World Recognition with Generalized Category Discovery [paper] [code]
  • Novel Categories Discovery from probability matrix perspective [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]
  • Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning [paper]
  • CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery [paper]
  • Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery [paper]

2024

  • Online Continuous Generalized Category Discovery (ECCV 2024) [paper] [code]
  • PromptCCD: Learning Gaussian Mixture Prompt Pool for Continual Category Discovery (ECCV 2024) [paper] [code]
  • Self-Cooperation Knowledge Distillation for Novel Class Discovery (ECCV 2024) [paper]
  • Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation (ECCV 2024) [paper] [code]
  • Contextuality Helps Representation Learning for Generalized Category Discovery (ICIP 2024) [paper] [code]
  • NC-NCD: Novel Class Discovery for Node Classification (CIKM 2024) [paper]
  • A Practical Approach to Novel Class Discovery in Tabular Data (DMKD 2024) [paper] [code]
  • Novel Class Discovery for Ultra-Fine-Grained Visual Categorization (CVPR 2024) [paper] [code]
  • Contrastive Mean-Shift Learning for Generalized Category Discovery (CVPR 2024) [paper] [code]
  • CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery (CVPR Workshop 2024) [paper]
  • Active Generalized Category Discovery (CVPR 2024) [paper] [code]
  • Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling (CVPR 2024) [paper]
  • Federated Generalized Category Discovery (CVPR 2024) [paper]
  • Democratizing Fine-grained Visual Recognition with Large Language Models (ICLR 2024) [paper] [project]
  • SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning (ICLR 2024) [paper] [code]
  • A Unified Knowledge Transfer Network for Generalized Category Discovery (AAAI 2024)
  • Novel Class Discovery in Chest X-Rays via Paired Images and Text (AAAI 2024) [framework]
  • Semantic-Guided Novel Category Discovery (AAAI 2024) [paper] [code]
  • Adaptive Discovering and Merging for Incremental Novel Class Discovery (AAAI 2024) [paper]
  • Debiased Novel Category Discovering and Localization (AAAI 2024) [paper]
  • Transfer and Alignment Network for Generalized Category Discovery (AAAI 2024) [paper] [code]
  • Guided Cluster Aggregation: A Hierarchical Approach to Generalized Category Discovery (WACV 2024) [paper] [code]
  • AMEND: Adaptive Margin and Expanded Neighborhood for Efficient Generalized Category Discovery (WACV 2024) [paper] [code]

2023

  • Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting (EMNLP 2023) [paper] [code]
  • A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning (NeurIPS 2023) [paper] [code]
  • Decompose Novel into Known: Part Concept Learning For 3D Novel Class Discovery (NeurIPS 2023) [paper]
  • Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery (NeurIPS 2023) [paper] [code]
  • Towards Distribution-Agnostic Generalized Category Discovery (NeurIPS 2023) [paper] [code]
  • No Representation Rules Them All in Category Discovery (NeurIPS 2023) [paper] [code]
  • Discover and Align Taxonomic Context Priors for Open-world Semi-Supervised Learning (NeurIPS 2023) [paper] [code]
  • Generalized Category Discovery with Clustering Assignment Consistency (ICONIP 2023) [paper]
  • Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering (MICCAI 2023) [paper]
  • Novel Class Discovery for Long-tailed Recognition (TMLR 2023) [paper]
  • Generalized Categories Discovery for Long-tailed Recognition (ICCV Workshop 2023) [paper]
  • Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier (ICCV 2023) [paper] [code]
  • Parametric Information Maximization for Generalized Category Discovery (ICCV 2023) [paper] [code]
  • MetaGCD: Learning to Continually Learn in Generalized Category Discovery (ICCV 2023) [paper] [code]
  • Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery (ICCV 2023) [paper] [code]
  • Class-relation Knowledge Distillation for Novel Class Discovery (ICCV 2023) [paper]
  • Incremental Generalized Category Discovery (ICCV 2023) [paper] [code]
  • Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery (ICCV 2023) [paper] [code]
  • 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]
  • Open-world Semi-supervised Novel Class Discovery (IJCAI 2023) [paper] [code]
  • ImbaGCD: Imbalanced Generalized Category Discovery (CVPR Workshop 2023) [paper]
  • On-the-Fly Category Discovery (CVPR 2023) [paper] [code]
  • Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery (CVPR 2023) [paper] [code]
  • 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]

2022

  • 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]
  • Robust Semi-Supervised Learning when Not All Classes have Labels (NeurIPS 2022) [paper] [code]
  • 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]
  • Towards Realistic Semi-Supervised Learning (ECCV 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]
  • 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]

2021

  • 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]

2020

  • 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]

2019

  • 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)

2018

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

2016

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

Contributing

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