Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2020
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| author | Yeh, Jia-Fong Lee, Hsin-Ying Tsai, Bing-Chen Chen, Yi-Rong Huang, Ping-Chia Hsu, Winston H. |
| author_facet | Yeh, Jia-Fong Lee, Hsin-Ying Tsai, Bing-Chen Chen, Yi-Rong Huang, Ping-Chia Hsu, Winston H. |
| contents | In recent years, few-shot learning problems have received a lot of attention. While methods in most previous works were trained and tested on datasets in one single domain, cross-domain few-shot learning is a brand-new branch of few-shot learning problems, where models handle datasets in different domains between training and testing phases. In this paper, to solve the problem that the model is pre-trained (meta-trained) on a single dataset while fine-tuned on datasets in four different domains, including common objects, satellite images, and medical images, we propose a novel large margin fine-tuning method (LMM-PQS), which generates pseudo query images from support images and fine-tunes the feature extraction modules with a large margin mechanism inspired by methods in face recognition. According to the experiment results, LMM-PQS surpasses the baseline models by a significant margin and demonstrates that our approach is robust and can easily adapt pre-trained models to new domains with few data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2005_09218 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning Yeh, Jia-Fong Lee, Hsin-Ying Tsai, Bing-Chen Chen, Yi-Rong Huang, Ping-Chia Hsu, Winston H. Machine Learning In recent years, few-shot learning problems have received a lot of attention. While methods in most previous works were trained and tested on datasets in one single domain, cross-domain few-shot learning is a brand-new branch of few-shot learning problems, where models handle datasets in different domains between training and testing phases. In this paper, to solve the problem that the model is pre-trained (meta-trained) on a single dataset while fine-tuned on datasets in four different domains, including common objects, satellite images, and medical images, we propose a novel large margin fine-tuning method (LMM-PQS), which generates pseudo query images from support images and fine-tunes the feature extraction modules with a large margin mechanism inspired by methods in face recognition. According to the experiment results, LMM-PQS surpasses the baseline models by a significant margin and demonstrates that our approach is robust and can easily adapt pre-trained models to new domains with few data. |
| title | Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2005.09218 |