  Generalized Category Discovery (GCD) is a crucial task that aims to recognize
both known and novel categories from a set of unlabeled data by utilizing a few
labeled data with only known categories. Due to the lack of supervision and
category information, current methods usually perform poorly on novel
categories and struggle to reveal semantic meanings of the discovered clusters,
which limits their applications in the real world. To mitigate the above
issues, we propose Loop, an end-to-end active-learning framework that
introduces Large Language Models (LLMs) into the training loop, which can boost
model performance and generate category names without relying on any human
efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to
select samples that have a higher probability of falling to wrong clusters,
based on neighborhood prediction consistency and entropy of cluster assignment
probabilities. Then we propose a Scalable Query strategy to allow LLMs to
choose true neighbors of the selected samples from multiple candidate samples.
Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive
Learning (RNCL) to pull samples and their neighbors closer to learn
clustering-friendly representations. Finally, we select representative samples
from clusters corresponding to novel categories to allow LLMs to generate
category names for them. Extensive experiments on three benchmark datasets show
that Loop outperforms SOTA models by a large margin and generates accurate
category names for the discovered clusters. Code and data are available at
https://github.com/Lackel/LOOP.
