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J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p> Convolutional Neural Networks (CNNs) have shown exceptional performance across various domains; however, their deep architectures require extensive datasets for effective training. In practical situations, gathering a large number of training examples can be extremely challenging, and the model is at risk of overfitting due to the limited and potentially biased nature of the available data. To address this challenge, a novel approach using a deep migration hybrid model has been introduced to tackle the issues posed by small-sample datasets. Initially, transfer learning is utilized to convey the knowledge acquired by a pretrained network from the ImageNet database to a limited dataset. Subsequently, the fully connected layers and adaptive average pooling layers of the pretrained network are augmented by incorporating a spatial transformer network (STN) for further training. 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This confirms that our method is resilient to class imbalance and sample corruption. <\/jats:p>","DOI":"10.1142\/s0219691325500080","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T10:23:03Z","timestamp":1741256583000},"source":"Crossref","is-referenced-by-count":0,"title":["Robust small-sample classification via RN50STN architecture with adaptive example reweighting"],"prefix":"10.1142","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6502-587X","authenticated-orcid":false,"given":"Linchang","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computer Science, Guiyang University, Guizhou 550005, P. R. China"},{"name":"Guizhou Provincial Key Laboratory for Digital Protection, Development and Utilization of Cultural Heritage, Guiyang 550002, P. R. 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