Papers › Parametric Classification for Generalized Category Discovery: A Baseline Study
Parametric Classification for Generalized Category Discovery: A Baseline Study
Xin Wen, Bingchen Zhao, Xiaojuan Qi
Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. Previous studies argued that parametric classifiers are prone to overfitting to seen categories, and endorsed using a non-parametric classifier formed with semi-supervised k-means. However, in this study, we investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We demonstrate that two prediction biases exist: the classifier tends to predict seen classes more often, and produces an imbalanced distribution across seen and novel categories. Based on these findings, we propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers. We hope the investigation and proposed simple framework can serve as a strong baseline to facilitate future studies in this field. Our code is available at: https://github.com/CVMI-Lab/SimGCD.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open-World Semi-Supervised Learning | CIFAR-10 | SimGCD (ViT-B-16) | All accuracy (50% Labeled) | 97.0 | #4 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | CIFAR-10 | SimGCD (ViT-B-16) | Novel accuracy (50% Labeled) | 98.5 | #4 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | CIFAR-10 | SimGCD (ViT-B-16) | Seen accuracy (50% Labeled) | 93.9 | #4 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | ImageNet-100 (TEMI Split) | SimGCD (ViT-B-16) | All accuracy (50% Labeled) | 83.6 | #1 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | ImageNet-100 (TEMI Split) | SimGCD (ViT-B-16) | Novel accuracy (50% Labeled) | 79.1 | #1 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | ImageNet-100 (TEMI Split) | SimGCD (ViT-B-16) | Seen accuracy (50% Labeled) | 92.4 | #1 of 5 | Archive leaderboard | report |
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