title: ProtoGCD: Unified and Unbiased Prototype Learning for Generalized
    Category Discovery
summary: Generalized category discovery (GCD) is a pragmatic but underexplored
  problem, which requires models to automatically cluster and discover novel
  categories by leveraging the labeled samples from old classes. The challenge is
  that unlabeled data contain both old and new classes. Early works leveraging
  pseudo-labeling with parametric classifiers handle old and new classes
  separately, which brings about imbalanced accuracy between them. Recent methods
  employing contrastive learning neglect potential positives and are decoupled
  from the clustering objective, leading to biased representations and
  sub-optimal results. To address these issues, we introduce a unified and
  unbiased prototype learning framework, namely ProtoGCD, wherein old and new
  classes are modeled with joint prototypes and unified learning objectives,
  {enabling unified modeling between old and new classes}. Specifically, we
  propose a dual-level adaptive pseudo-labeling mechanism to mitigate
  confirmation bias, together with two regularization terms to collectively help
  learn more suitable representations for GCD. Moreover, for practical
  considerations, we devise a criterion to estimate the number of new classes.
  Furthermore, we extend ProtoGCD to detect unseen outliers, achieving task-level
  unification. Comprehensive experiments show that ProtoGCD achieves
  state-of-the-art performance on both generic and fine-grained datasets. The
  code is available at https://github.com/mashijie1028/ProtoGCD.
paper_authors._id: 67f69b897245fd7afd5a6151
paper_authors.name: Shijie Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-09T16:21:27.370Z
paper_authors._id: 67d30d9ae45dc43004b31425
paper_authors.avatarUrl: /avatars/714f4c88710ab4cc75dda49c76dd3b09.svg
paper_authors.fullname: Shijie Ma
paper_authors.type: user
paper_authors.user: msj9817
paper_authors._id: 67f69b897245fd7afd5a6152
paper_authors.name: Fei Zhu
paper_authors._id: 67f69b897245fd7afd5a6153
paper_authors.name: Xu-Yao Zhang
paper_authors._id: 67f69b897245fd7afd5a6154
paper_authors.name: Cheng-Lin Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-09T16:51:25.922Z
paper_authors._id: 6644ba133744e4bcd5b39591
paper_authors.avatarUrl: /avatars/714b3e5f2b38d8e1192ee0c7f4b0ce9a.svg
paper_authors.fullname: Chenglin Liu
paper_authors.type: user
paper_authors.user: Serenity-abc
paper_title: ProtoGCD: Unified and Unbiased Prototype Learning for Generalized
    Category Discovery
paper_summary: Generalized category discovery (GCD) is a pragmatic but underexplored
  problem, which requires models to automatically cluster and discover novel
  categories by leveraging the labeled samples from old classes. The challenge is
  that unlabeled data contain both old and new classes. Early works leveraging
  pseudo-labeling with parametric classifiers handle old and new classes
  separately, which brings about imbalanced accuracy between them. Recent methods
  employing contrastive learning neglect potential positives and are decoupled
  from the clustering objective, leading to biased representations and
  sub-optimal results. To address these issues, we introduce a unified and
  unbiased prototype learning framework, namely ProtoGCD, wherein old and new
  classes are modeled with joint prototypes and unified learning objectives,
  {enabling unified modeling between old and new classes}. Specifically, we
  propose a dual-level adaptive pseudo-labeling mechanism to mitigate
  confirmation bias, together with two regularization terms to collectively help
  learn more suitable representations for GCD. Moreover, for practical
  considerations, we devise a criterion to estimate the number of new classes.
  Furthermore, we extend ProtoGCD to detect unseen outliers, achieving task-level
  unification. Comprehensive experiments show that ProtoGCD achieves
  state-of-the-art performance on both generic and fine-grained datasets. The
  code is available at https://github.com/mashijie1028/ProtoGCD.
paper_ai_summary: ProtoGCD is a unified prototype learning framework that improves generalized category discovery by jointly modeling old and new classes with adaptive pseudo-labeling and regularization.
paper_ai_keywords: generalized category discovery GCD pseudo-labeling parametric classifiers contrastive learning prototype learning dual-level adaptive pseudo-labeling regularization unseen outliers task-level unification