{"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/explanation-guided-training-for-cross-domain","title":"Explanation-Guided Training for Cross-Domain Few-Shot Classification","arxiv_id":"2007.08790","date":"2020-07-17","proceeding":null,"authors":["Jiamei Sun","Sebastian Lapuschkin","Wojciech Samek","Yunqing Zhao","Ngai-Man Cheung","Alexander Binder"],"abstract":"Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces challenges originating from the limited labeled data in each class and, additionally, from the domain shift between training and test sets. In this paper, we introduce a novel training approach for existing FSC models. It leverages on the explanation scores, obtained from existing explanation methods when applied to the predictions of FSC models, computed for intermediate feature maps of the models. Firstly, we tailor the layer-wise relevance propagation (LRP) method to explain the predictions of FSC models. Secondly, we develop a model-agnostic explanation-guided training strategy that dynamically finds and emphasizes the features which are important for the predictions. Our contribution does not target a novel explanation method but lies in a novel application of explanations for the training phase. We show that explanation-guided training effectively improves the model generalization. We observe improved accuracy for three different FSC models: RelationNet, cross attention network, and a graph neural network-based formulation, on five few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae. The source code is available at https://github.com/SunJiamei/few-shot-lrp-guided","url_abs":"https://arxiv.org/abs/2007.08790v2","url_pdf":"https://arxiv.org/pdf/2007.08790v2.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":"explanation-guided-training-for-cross-domain","repo_url":"https://github.com/SunJiamei/few-shot-lrp-guided","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"cross-domain-few-shot","task_name":"Cross-Domain Few-Shot"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/cross-domain-few-shot-on-chestx","task":"Cross-Domain Few-Shot","dataset":"ChestX","model":"LRP","rank_in_archive_order":10,"of":11,"metrics":{"5 shot":"24.53"},"uses_additional_data":false},{"leaderboard":"/sota/cross-domain-few-shot-on-eurosat","task":"Cross-Domain Few-Shot","dataset":"EuroSAT","model":"LRP","rank_in_archive_order":11,"of":11,"metrics":{"5 shot":"77.14"},"uses_additional_data":false},{"leaderboard":"/sota/cross-domain-few-shot-on-isic2018","task":"Cross-Domain Few-Shot","dataset":"ISIC2018","model":"LRP","rank_in_archive_order":9,"of":11,"metrics":{"5 shot":"44.14"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2007.08790","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}