{"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/tailoring-self-supervision-for-supervised","title":"Tailoring Self-Supervision for Supervised Learning","arxiv_id":"2207.10023","date":"2022-07-20","proceeding":null,"authors":["WonJun Moon","Ji-Hwan Kim","Jae-Pil Heo"],"abstract":"Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tasks are specialized for unsupervised representation learning. To this end, we begin by presenting three desirable properties for such auxiliary tasks to assist the supervised objective. First, the tasks need to guide the model to learn rich features. Second, the transformations involved in the self-supervision should not significantly alter the training distribution. Third, the tasks are preferred to be light and generic for high applicability to prior arts. Subsequently, to show how existing pretext tasks can fulfill these and be tailored for supervised learning, we propose a simple auxiliary self-supervision task, predicting localizable rotation (LoRot). Our exhaustive experiments validate the merits of LoRot as a pretext task tailored for supervised learning in terms of robustness and generalization capability. Our code is available at https://github.com/wjun0830/Localizable-Rotation.","url_abs":"https://arxiv.org/abs/2207.10023v1","url_pdf":"https://arxiv.org/pdf/2207.10023v1.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":"tailoring-self-supervision-for-supervised","repo_url":"https://github.com/wjun0830/localizable-rotation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"adversarial-robustness","task_name":"Adversarial Robustness"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"ood-detection","task_name":"Out of Distribution (OOD) Detection"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"imbalanced-classification","task_name":"imbalanced classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/data-augmentation-on-imagenet","task":"Data Augmentation","dataset":"ImageNet","model":"ResNet-50 (LoRot-E)","rank_in_archive_order":9,"of":17,"metrics":{"Accuracy (%)":"77.72"},"uses_additional_data":false},{"leaderboard":"/sota/data-augmentation-on-imagenet","task":"Data Augmentation","dataset":"ImageNet","model":"ResNet-50 (LoRot-I)","rank_in_archive_order":10,"of":17,"metrics":{"Accuracy (%)":"77.71"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.10023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10023"}},"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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