{"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/mergednet-a-simple-approach-for-one-shot","title":"MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers","arxiv_id":null,"date":"2022-10-14","proceeding":"Neurocomputing 2022 10","authors":["John Atanbori","Samuel Rose"],"abstract":"Classifiers trained on disjointed classes with few labelled data points are used in one-shot learning to identify visual concepts from other classes. Recently, Siamese networks and similarity layers have been used to solve the one-shot learning problem, achieving state-of-the-art performance on visual-character recognition datasets. Various techniques have been developed over the years to improve the performance of these networks on fine-grained image classification datasets. They focused primarily on improving the loss and activation functions, augmenting visual features, employing multiscale metric learning, and pre-training and fine-tuning the backbone network. We investigate similarity layers for one-shot learning tasks and propose two frameworks for combining these layers into a MergedNet network. On all four datasets used in our experiment, MergedNet outperformed the baselines based on classification accuracy, and it generalises to other datasets when trained on miniImageNet.","url_abs":"https://doi.org/10.1016/j.neucom.2022.08.070","url_pdf":"https://doi.org/10.1016/j.neucom.2022.08.070","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":"mergednet-a-simple-approach-for-one-shot","repo_url":"https://github.com/Amotica/MergedNET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"fine-grained-image-classification","task_name":"Fine-Grained Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"metric-learning","task_name":"Metric Learning"},{"task_slug":"one-shot-learning","task_name":"One-Shot Learning"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/few-shot-image-classification-on-cub-200-5-1","task":"Few-Shot Image Classification","dataset":"CUB 200 5-way 1-shot","model":"MergedNet-Max","rank_in_archive_order":25,"of":36,"metrics":{"Accuracy":"75.34"},"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":"MergedNet-Max","rank_in_archive_order":27,"of":32,"metrics":{"Accuracy":"83.42"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-caltech-256","task":"Few-Shot Image Classification","dataset":"Caltech-256 5-way (1-shot)","model":"MergedNet-Max","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"65.77"},"uses_additional_data":false},{"leaderboard":"/sota/few-shot-image-classification-on-caltech-256-1","task":"Few-Shot Image Classification","dataset":"Caltech-256 5-way (5-shot)","model":"MergedNet-Concat","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"81.34"},"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":"MergedNet-Max","rank_in_archive_order":40,"of":105,"metrics":{"Accuracy":"68.05"},"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":"MergedNet-Max","rank_in_archive_order":51,"of":95,"metrics":{"Accuracy":"80.40"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}