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- W4292133509 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." @default.
- W4292133509 created "2022-08-17" @default.
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- W4292133509 date "2022-10-01" @default.
- W4292133509 modified "2023-09-27" @default.
- W4292133509 title "MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers" @default.
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- W4292133509 doi "https://doi.org/10.1016/j.neucom.2022.08.070" @default.
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