{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-simple-parametric-classification-baseline","title":"Parametric Classification for Generalized Category Discovery: A Baseline Study","arxiv_id":"2211.11727","date":"2022-11-21","proceeding":"ICCV 2023 1","authors":["Xin Wen","Bingchen Zhao","Xiaojuan Qi"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2211.11727v4","url_pdf":"https://arxiv.org/pdf/2211.11727v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-simple-parametric-classification-baseline","repo_url":"https://github.com/cvmi-lab/simgcd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-simple-parametric-classification-baseline","repo_url":"https://github.com/mashijie1028/activegcd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"novel-class-discovery","task_name":"Novel Class Discovery"},{"task_slug":"open-world-semi-supervised-learning","task_name":"Open-World Semi-Supervised Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/open-world-semi-supervised-learning-on-cifar","task":"Open-World Semi-Supervised Learning","dataset":"CIFAR-10","model":"SimGCD (ViT-B-16)","rank_in_archive_order":4,"of":5,"metrics":{"All accuracy (50% Labeled)":"97.0","Novel accuracy (50% Labeled)":"98.5","Seen accuracy (50% Labeled)":"93.9"},"uses_additional_data":false},{"leaderboard":"/sota/open-world-semi-supervised-learning-on-1","task":"Open-World Semi-Supervised Learning","dataset":"ImageNet-100 (TEMI Split)","model":"SimGCD (ViT-B-16)","rank_in_archive_order":1,"of":5,"metrics":{"All accuracy (50% Labeled)":"83.6","Novel accuracy (50% Labeled)":"79.1","Seen accuracy (50% Labeled)":"92.4"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.11727","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.11727"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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