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Cross-domain self-supervised few-shot learning via multiple crops with teacher-student network.
- Source :
-
Engineering Applications of Artificial Intelligence . Jun2024, Vol. 132, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Most few-shot learning(FSL) methods rely on a pre-trained network on a large annotated base dataset with a feature distribution similar to that of the target domain. Conventional transfer learning and traditional few-shot learning methods are ineffective when there is a large gap between the source and target domain. We propose a simple teacher-student network solution to facilitate unlabeled images from the target domain to alleviate domain gap. We impose a self-supervised loss by calculating predictions from large crops of the unannotated samples of target domain using a teacher network and matching them with small crops of the same images from a student network. Furthermore, we design a novel contrastive loss for large crops to sufficiently utilize the self-supervised information of unlabeled images on target domain for the model training. The feature representation can be easily generalized to the target domain without the pretraining phase on target-specific classes. The accuracies of our model are 23. 61 ± 0. 42 , 33. 87 ± 0. 59 , 63. 21 ± 0. 88 , 74. 36 ± 0. 88 on ChestX, ISIC, EuroSAT, and CropDisease datasets for the 1-shot scenario respectively. Extensive experiments show that the proposed method achieves competitive performance on the challenging cross-domain FSL image classification. [ABSTRACT FROM AUTHOR]
- Subjects :
- *IMAGE recognition (Computer vision)
*CROP losses
*CROPS
*LEARNING
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 132
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 177088638
- Full Text :
- https://doi.org/10.1016/j.engappai.2024.107892