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Few-Shot Classification with Dual-Model Deep Feature Extraction and Similarity Measurement
- Source :
- Electronics; Volume 11; Issue 21; Pages: 3502
- Publication Year :
- 2022
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2022.
-
Abstract
- From traditional machine learning to the latest deep learning classifiers, most models require a large amount of labeled data to perform optimal training and obtain the best performance. Yet, when limited training samples are available or when accompanied by noisy labels, severe degradation in accuracy can arise. The proposed work mainly focusses on these practical issues. Herein, standard datasets, i.e., Mini-ImageNet, CIFAR-FS, and CUB 200, are considered, which also have similar issues. The main goal is to utilize a few labeled data in the training stage, extracting image features and then performing feature similarity analysis across all samples. The highlighted aspects of the proposed method are as follows. (1) The main self-supervised learning strategies and augmentation techniques are exploited to obtain the best pretrained model. (2) An improved dual-model mechanism is proposed to train the support and query datasets with multiple training configurations. As examined in the experiments, the dual-model approach obtains superior performance of few-shot classification compared with all of the state-of-the-art methods.
Details
- Language :
- English
- ISSN :
- 20799292
- Database :
- OpenAIRE
- Journal :
- Electronics; Volume 11; Issue 21; Pages: 3502
- Accession number :
- edsair.doi.dedup.....62f776756810d638722643f60e6dc9ab
- Full Text :
- https://doi.org/10.3390/electronics11213502