{"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/image-classification-on-small-datasets-via","title":"ChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing","arxiv_id":"2202.11616","date":"2022-02-23","proceeding":null,"authors":["Christoph Reinders","Frederik Schubert","Bodo Rosenhahn"],"abstract":"Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed architecture called ChimeraMix learns a data augmentation by generating compositions of instances. The generative model encodes images in pairs, combines the features guided by a mask, and creates new samples. For evaluation, all methods are trained from scratch without any additional data. Several experiments on benchmark datasets, e.g. ciFAIR-10, STL-10, and ciFAIR-100, demonstrate the superior performance of ChimeraMix compared to current state-of-the-art methods for classification on small datasets.","url_abs":"https://arxiv.org/abs/2202.11616v2","url_pdf":"https://arxiv.org/pdf/2202.11616v2.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":"image-classification-on-small-datasets-via","repo_url":"https://github.com/creinders/chimeramix","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"small-data","task_name":"Small Data Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/small-data-image-classification-on-cifar-10-2","task":"Small Data Image Classification","dataset":"CIFAR-10, 100 Labels","model":"ChimeraMix+AutoAugment","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy (%)":"49.75"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifar-10-2","task":"Small Data Image Classification","dataset":"CIFAR-10, 100 Labels","model":"ChimeraMix","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy (%)":"47.6"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifar-10-3","task":"Small Data Image Classification","dataset":"CIFAR-10, 1000 Labels","model":"ChimeraMix+AutoAugment","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy (%)":"76.76"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifar-10-3","task":"Small Data Image Classification","dataset":"CIFAR-10, 1000 Labels","model":"ChimeraMix","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy (%)":"74.96"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifar-10","task":"Small Data Image Classification","dataset":"CIFAR-10, 500 Labels","model":"ChimeraMix+AutoAugment","rank_in_archive_order":1,"of":6,"metrics":{"Accuracy (%)":"70.09"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifar-10","task":"Small Data Image Classification","dataset":"CIFAR-10, 500 Labels","model":"ChimeraMix","rank_in_archive_order":2,"of":6,"metrics":{"Accuracy (%)":"67.3"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-on-cifar-100-1000-labels-1","task":"Small Data Image Classification","dataset":"CIFAR-100, 1000 Labels","model":"ChimeraMix+AutoAugment","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy":"35.02"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-on-cifar-100-1000-labels-1","task":"Small Data Image Classification","dataset":"CIFAR-100, 1000 Labels","model":"ChimeraMix","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"32.72"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifair-10-1","task":"Small Data Image Classification","dataset":"ciFAIR-10 50 samples per class","model":"ChimeraMix+AutoAugment","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy":"70.09"},"uses_additional_data":false},{"leaderboard":"/sota/small-data-image-classification-on-cifair-10-1","task":"Small Data Image Classification","dataset":"ciFAIR-10 50 samples per class","model":"ChimeraMix","rank_in_archive_order":2,"of":5,"metrics":{"Accuracy":"67.30"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2202.11616","atlas_url":"https://app.syntology.ai/?focus=2202.11616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.11616"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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