ProtoGCD: Unified and Unbiased Prototype Learning for Generalized
  Category Discovery
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.
67f69b897245fd7afd5a6151
Shijie Ma
claimed_verified
2025-04-09T16:21:27.370Z
67d30d9ae45dc43004b31425
/avatars/714f4c88710ab4cc75dda49c76dd3b09.svg
Shijie Ma
user
msj9817
67f69b897245fd7afd5a6152
Fei Zhu
67f69b897245fd7afd5a6153
Xu-Yao Zhang
67f69b897245fd7afd5a6154
Cheng-Lin Liu
admin_assigned
2025-04-09T16:51:25.922Z
6644ba133744e4bcd5b39591
/avatars/714b3e5f2b38d8e1192ee0c7f4b0ce9a.svg
Chenglin Liu
user
Serenity-abc
ProtoGCD: Unified and Unbiased Prototype Learning for Generalized
  Category Discovery
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.
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.
generalized category discovery GCD pseudo-labeling parametric classifiers contrastive learning prototype learning dual-level adaptive pseudo-labeling regularization unseen outliers task-level unification