{"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/instance-credibility-inference-for-few-shot","title":"Instance Credibility Inference for Few-Shot Learning","arxiv_id":"2003.11853","date":"2020-03-26","proceeding":"CVPR 2020 6","authors":["Yikai Wang","Chengming Xu","Chen Liu","Li Zhang","Yanwei Fu"],"abstract":"Few-shot learning (FSL) aims to recognize new objects with extremely limited training data for each category. Previous efforts are made by either leveraging meta-learning paradigm or novel principles in data augmentation to alleviate this extremely data-scarce problem. In contrast, this paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI) to exploit the distribution support of unlabeled instances for few-shot learning. Specifically, we first train a linear classifier with the labeled few-shot examples and use it to infer the pseudo-labels for the unlabeled data. To measure the credibility of each pseudo-labeled instance, we then propose to solve another linear regression hypothesis by increasing the sparsity of the incidental parameters and rank the pseudo-labeled instances with their sparsity degree. We select the most trustworthy pseudo-labeled instances alongside the labeled examples to re-train the linear classifier. This process is iterated until all the unlabeled samples are included in the expanded training set, i.e. the pseudo-label is converged for unlabeled data pool. Extensive experiments under two few-shot settings show that our simple approach can establish new state-of-the-arts on four widely used few-shot learning benchmark datasets including miniImageNet, tieredImageNet, CIFAR-FS, and CUB. Our code is available at: https://github.com/Yikai-Wang/ICI-FSL","url_abs":"https://arxiv.org/abs/2003.11853v2","url_pdf":"https://arxiv.org/pdf/2003.11853v2.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":"instance-credibility-inference-for-few-shot","repo_url":"https://github.com/Yikai-Wang/ICI-FSL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"}],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"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":"ICI","rank_in_archive_order":21,"of":38,"metrics":{"Accuracy":"76.51"},"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":"ICI","rank_in_archive_order":33,"of":39,"metrics":{"Accuracy":"84.32"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-cub-200-5-1","task":"Few-Shot Image Classification","dataset":"CUB 200 5-way 1-shot","model":"ICI","rank_in_archive_order":12,"of":36,"metrics":{"Accuracy":"89.58"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-cub-200-5","task":"Few-Shot Image Classification","dataset":"CUB 200 5-way 5-shot","model":"ICI","rank_in_archive_order":12,"of":32,"metrics":{"Accuracy":"92.48"},"uses_additional_data":true},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet","task":"Few-Shot Image Classification","dataset":"Dirichlet Mini-Imagenet (5-way, 1-shot)","model":"LR-ICI","rank_in_archive_order":8,"of":12,"metrics":{"1:1 Accuracy":"58.7"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-1","task":"Few-Shot Image Classification","dataset":"Dirichlet Mini-Imagenet (5-way, 5-shot)","model":"LR-ICI","rank_in_archive_order":9,"of":12,"metrics":{"1:1 Accuracy":"73.5"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-2","task":"Few-Shot Image Classification","dataset":"Dirichlet Tiered-Imagenet (5-way, 1-shot)","model":"LR+ICI","rank_in_archive_order":2,"of":9,"metrics":{"1:1 Accuracy":"74.6"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-dirichlet-3","task":"Few-Shot Image Classification","dataset":"Dirichlet Tiered-Imagenet (5-way, 5-shot)","model":"LR+ICI","rank_in_archive_order":4,"of":9,"metrics":{"1:1 Accuracy":"85.1"},"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":"ICI","rank_in_archive_order":36,"of":105,"metrics":{"Accuracy":"69.66"},"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":"ICI","rank_in_archive_order":53,"of":95,"metrics":{"Accuracy":"80.11"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-tiered","task":"Few-Shot Image Classification","dataset":"Tiered ImageNet 5-way (1-shot)","model":"ICI","rank_in_archive_order":7,"of":49,"metrics":{"Accuracy":"84.01"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-tiered-1","task":"Few-Shot Image Classification","dataset":"Tiered ImageNet 5-way (5-shot)","model":"ICI","rank_in_archive_order":12,"of":51,"metrics":{"Accuracy":"89.00"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.11853","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}