{"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/task-augmentation-by-rotating-for-meta","title":"Task Augmentation by Rotating for Meta-Learning","arxiv_id":"2003.00804","date":"2020-02-08","proceeding":"arXiv 2020 2","authors":["Jialin Liu","Fei Chao","Chih-Min Lin"],"abstract":"Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-learning. In this paper, we introduce a task augmentation method by rotating, which increases the number of classes by rotating the original images 90, 180 and 270 degrees, different from traditional augmentation methods which increase the number of images. With a larger amount of classes, we can sample more diverse task instances during training. Therefore, task augmentation by rotating allows us to train a deep network by meta-learning methods with little over-fitting. Experimental results show that our approach is better than the rotation for increasing the number of images and achieves state-of-the-art performance on miniImageNet, CIFAR-FS, and FC100 few-shot learning benchmarks. The code is available on \\url{www.github.com/AceChuse/TaskLevelAug}.","url_abs":"https://arxiv.org/abs/2003.00804v1","url_pdf":"https://arxiv.org/pdf/2003.00804v1.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":"task-augmentation-by-rotating-for-meta","repo_url":"https://github.com/AceChuse/TaskLevelAug","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-cifar-fs-5","task":"Few-Shot Image Classification","dataset":"CIFAR-FS 5-way (1-shot)","model":"R2-D2+Task Aug","rank_in_archive_order":18,"of":38,"metrics":{"Accuracy":"77.66"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-cifar-fs-5","task":"Few-Shot Image Classification","dataset":"CIFAR-FS 5-way (1-shot)","model":"MetaOptNet-SVM+Task Aug","rank_in_archive_order":20,"of":38,"metrics":{"Accuracy":"76.75"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-cifar-fs-5-1","task":"Few-Shot Image Classification","dataset":"CIFAR-FS 5-way (5-shot)","model":"MetaOptNet-SVM+Task Aug","rank_in_archive_order":20,"of":39,"metrics":{"Accuracy":"88.38"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-cifar-fs-5-1","task":"Few-Shot Image Classification","dataset":"CIFAR-FS 5-way (5-shot)","model":"R2-D2+Task Aug","rank_in_archive_order":22,"of":39,"metrics":{"Accuracy":"88.33"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-fc100-5-way","task":"Few-Shot Image Classification","dataset":"FC100 5-way (1-shot)","model":"R2-D2+Task Aug","rank_in_archive_order":4,"of":22,"metrics":{"Accuracy":"51.35"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-fc100-5-way","task":"Few-Shot Image Classification","dataset":"FC100 5-way (1-shot)","model":"MetaOptNet-SVM+Task Aug","rank_in_archive_order":6,"of":22,"metrics":{"Accuracy":"49.77"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-fc100-5-way-1","task":"Few-Shot Image Classification","dataset":"FC100 5-way (5-shot)","model":"R2-D2+Task Aug","rank_in_archive_order":2,"of":22,"metrics":{"Accuracy":"67.66"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-fc100-5-way-1","task":"Few-Shot Image Classification","dataset":"FC100 5-way (5-shot)","model":"MetaOptNet-SVM+Task Aug","rank_in_archive_order":3,"of":22,"metrics":{"Accuracy":"67.17"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-1","task":"Few-Shot Image Classification","dataset":"Mini-ImageNet - 1-Shot Learning","model":"R2-D2+Task Aug","rank_in_archive_order":10,"of":16,"metrics":{"Accuracy":"65.95%"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-1","task":"Few-Shot Image Classification","dataset":"Mini-ImageNet - 1-Shot Learning","model":"MetaOptNet-SVM+Task Aug","rank_in_archive_order":11,"of":16,"metrics":{"Accuracy":"65.38%"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-2","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (1-shot)","model":"R2-D2+Task Aug","rank_in_archive_order":52,"of":105,"metrics":{"Accuracy":"65.95"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-2","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (1-shot)","model":"MetaOptNet-SVM+Task Aug","rank_in_archive_order":56,"of":105,"metrics":{"Accuracy":"65.38"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-3","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (5-shot)","model":"MetaOptNet-SVM+Task Aug","rank_in_archive_order":43,"of":95,"metrics":{"Accuracy":"82.13"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-mini-3","task":"Few-Shot Image Classification","dataset":"Mini-Imagenet 5-way (5-shot)","model":"R2-D2+Task Aug","rank_in_archive_order":44,"of":95,"metrics":{"Accuracy":"81.96"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.00804","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}