Papers › Generalized Category Discovery
Generalized Category Discovery
Sagar Vaze, Kai Han, Andrea Vedaldi, Andrew Zisserman
In this paper, we consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled set. Here, the unlabelled images may come from labelled classes or from novel ones. Existing recognition methods are not able to deal with this setting, because they make several restrictive assumptions, such as the unlabelled instances only coming from known - or unknown - classes, and the number of unknown classes being known a-priori. We address the more unconstrained setting, naming it 'Generalized Category Discovery', and challenge all these assumptions. We first establish strong baselines by taking state-of-the-art algorithms from novel category discovery and adapting them for this task. Next, we propose the use of vision transformers with contrastive representation learning for this open-world setting. We then introduce a simple yet effective semi-supervised k-means method to cluster the unlabelled data into seen and unseen classes automatically, substantially outperforming the baselines. Finally, we also propose a new approach to estimate the number of classes in the unlabelled data. We thoroughly evaluate our approach on public datasets for generic object classification and on fine-grained datasets, leveraging the recent Semantic Shift Benchmark suite. Project page at https://www.robots.ox.ac.uk/~vgg/research/gcd
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Code
Syntology Ran 5 of 11 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 1 ran · fixture could not drive it; 4 ran with no contract checked.
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Code Syntology ran Syntology
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Tasks
Datasets
Introduced by this paper, per the archive.
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 | GCD (ViT-B-16) | All accuracy (50% Labeled) | 91.5 | #5 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | CIFAR-10 | GCD (ViT-B-16) | Novel accuracy (50% Labeled) | 88.2 | #5 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | CIFAR-10 | GCD (ViT-B-16) | Seen accuracy (50% Labeled) | 97.9 | #5 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | ImageNet-100 (TEMI Split) | GCD (ViT-B-16) | All accuracy (50% Labeled) | 74.1 | #4 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | ImageNet-100 (TEMI Split) | GCD (ViT-B-16) | Novel accuracy (50% Labeled) | 66.3 | #4 of 5 | Archive leaderboard | report |
| Open-World Semi-Supervised Learning | ImageNet-100 (TEMI Split) | GCD (ViT-B-16) | Seen accuracy (50% Labeled) | 89.8 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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