Computer Science > Computer Vision and Pattern Recognition
arXiv:2301.11015 (cs)
[Submitted on 26 Jan 2023]
Title:Explore the Power of Dropout on Few-shot Learning
View a PDF of the paper titled Explore the Power of Dropout on Few-shot Learning, by Shaobo Lin and 2 other authors
View PDFAbstract:The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. Extensive experiments on the few-shot object detection and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness of our method.
| Comments: | arXiv admin note: substantial text overlap witharXiv:2210.06409 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2301.11015 [cs.CV] |
| (orarXiv:2301.11015v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2301.11015 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Explore the Power of Dropout on Few-shot Learning, by Shaobo Lin and 2 other authors
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