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DAC: Disentanglement-and-Calibration Module for Cross-Domain Few-Shot Classification
- Source :
- IEEE Access, Vol 11, Pp 82665-82673 (2023)
- Publication Year :
- 2023
- Publisher :
- IEEE, 2023.
-
Abstract
- Cross-domain few-shot classification (CD-FSC) aims to develop few-shot classification models trained on seen domains but tested on unseen domains. However, the cross-domain setup poses a challenge in the form of domain shift between the training and testing domains. Previous research has demonstrated that the encoder can disentangle features into domain-shared and domain-specific features. However, poorly estimated domain-specific features can lead to inadequate generalization on the unseen domain. This paper proposes a disentanglement-and-calibration module (DAC) to address this issue. The disentanglement component separates the features into domain-shared and domain-specific features, while the calibration component corrects the domain-specific features. We demonstrate that the DAC module can significantly enhance the generalization capability of several baseline methods. Furthermore, we show that MatchingNet with the DAC module outperforms existing state-of-the-art methods by 10%-11% when trained on mini-ImageNet, CUB-200, Cars196, Places365 and tested on Plantae dataset.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 11
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.25d389a03943fdad5ea96385f59365
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2023.3294984