Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery

• URL: http://arxiv.org/abs/2605.08183v1
• Date: Tue, 05 May 2026 10:14:30 GMT
• Title: Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery
• Abstract summary: Generalized Category Discovery (GCD) seeks to identify novel categories from unlabeled data.<n>Prior GCD methods commonly leverage transferable representations from pre-trained models.<n>We propose LAGCD, a simple yet effective GCD approach that embeds a residual linear adapter into each ViT block.
• Score: 54.695641187741536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalized Category Discovery (GCD) seeks to identify novel categories from unlabeled data while retaining the classification ability of seen categories. Prior GCD methods commonly leverage transferable representations from pre-trained models, adapting to downstream datasets via partial fine-tuning (updating only the final ViT block) and visual prompt tuning (appending learnable vectors to inputs). However, conventional partial fine-tuning offers limited flexibility, as it fails to adapt the entire model; meanwhile, visual prompt tuning is prone to overfitting, due to its sensitivity to initialization and inherently constrained capacity. To address these limitations, we propose LAGCD, a simple yet effective GCD approach that embeds a residual linear adapter into each ViT block. From the perspective of feature sparsity, we systematically show that non-linearity in conventional adapters impairs performance, whereas our linear adapter enhances it by enabling more flexible model capacity. We further introduce an auxiliary distribution alignment loss to mitigate the negative impact of biased predictions between seen and novel categories. Extensive experiments on both generic and fine-grained datasets confirm that LAGCD consistently improves performance over many sophisticated baselines. The source code is available at https://github.com/yebo0216best/LAGCD

Related papers

• GRASP: Guided Residual Adapters with Sample-wise Partitioning [10.504309161945065]
  We propose GRASP: Guided Residual Adapters with Sample-wise Partitioning.<n>On the long-tail MIMIC-CXR-LT dataset, GRASP yields superior FID and diversity metrics, especially for rare classes.
  arXiv  Detail & Related papers  (2025-12-01T13:43:17Z)
• Compensating Distribution Drifts in Class-incremental Learning of Pre-trained Vision Transformers [27.14203097630326]
  We introduce a latent space transition operator and propose Sequential Learning with Drift Compensation.<n>SLDC aims to align feature distributions across tasks to mitigate the impact of drift.<n>Experiments on standard CIL benchmarks demonstrate that SLDC significantly improves the performance of SeqFT.
  arXiv  Detail & Related papers  (2025-11-13T03:40:54Z)
• Normalized Attention Guidance: Universal Negative Guidance for Diffusion Models [57.20761595019967]
  We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based normalization and refinement.<n>NAG restores effective negative guidance where CFG collapses while maintaining fidelity.<n>NAG generalizes across architectures (UNet, DiT), sampling regimes (few-step, multi-step), and modalities (image, video)
  arXiv  Detail & Related papers  (2025-05-27T13:30:46Z)
• HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters [53.97380482341493]
  "pre-train, prompt-tuning" has demonstrated impressive performance for tuning pre-trained heterogeneous graph neural networks (HGNNs) We propose a unified framework that combines two new adapters with potential labeled data extension to improve the generalization of pre-trained HGNN models.
  arXiv  Detail & Related papers  (2024-11-02T06:43:54Z)
• AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery [15.640670797555579]
  Generalized Category Discovery aims to classify old categories and discover new categories in the unlabeled data.<n>Existing studies on GCD typically devote to transferring the general knowledge from the self-supervised pretrained model to the target GCD task.<n>We propose a novel adapter-tuning-based method named AdaptGCD, which is the first work to introduce the adapter tuning into the GCD task.
  arXiv  Detail & Related papers  (2024-10-29T03:41:47Z)
• Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning [55.384428765798496]
  New data exhibits a long-tailed distribution, such as e-commerce platform reviews. This necessitates continuous model learning imbalanced data without forgetting. We introduce AdaPtive Adapter RouTing (APART) as an exemplar-free solution for LTCIL.
  arXiv  Detail & Related papers  (2024-09-11T17:52:00Z)
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery [76.82327473338734]
  Generalized category discovery (GCD) aims to automatically cluster partially labeled data. Unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. We propose a Dynamic Conceptional Contrastive Learning framework, which can effectively improve clustering accuracy.
  arXiv  Detail & Related papers  (2023-03-30T14:04:39Z)
• Fine-grained Retrieval Prompt Tuning [149.9071858259279]
  Fine-grained Retrieval Prompt Tuning steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompt and feature adaptation. Our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets.
  arXiv  Detail & Related papers  (2022-07-29T04:10:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.