{"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/vipriors-1-visual-inductive-priors-for-data","title":"VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges","arxiv_id":"2103.03768","date":"2021-03-05","proceeding":null,"authors":["Robert-Jan Bruintjes","Attila Lengyel","Marcos Baptista Rios","Osman Semih Kayhan","Jan van Gemert"],"abstract":"We present the first edition of \"VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning\" challenges. We offer four data-impaired challenges, where models are trained from scratch, and we reduce the number of training samples to a fraction of the full set. Furthermore, to encourage data efficient solutions, we prohibited the use of pre-trained models and other transfer learning techniques. The majority of top ranking solutions make heavy use of data augmentation, model ensembling, and novel and efficient network architectures to achieve significant performance increases compared to the provided baselines.","url_abs":"https://arxiv.org/abs/2103.03768v1","url_pdf":"https://arxiv.org/pdf/2103.03768v1.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":"vipriors-1-visual-inductive-priors-for-data","repo_url":"https://github.com/VIPriors/vipriors-challenges-toolkit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"coco-object-detection-vipriors-subset","name":"COCO Object Detection VIPriors subset","full_name":""},{"slug":"cityscapes-vipriors-subset","name":"Cityscapes VIPriors subset","full_name":""},{"slug":"imagenet-vipriors-subset","name":"ImageNet VIPriors subset","full_name":""},{"slug":"ucf-101-vipriors-subset","name":"UCF-101 VIPriors subset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.03768","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}