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Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data

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Deep learning models are difficult to obtain good performance when data is scarce and there are domain gaps. Cross-domain few-shot learning (CD-FSL) is designed to improve this problem. We propose an Ensemble Model with Batch Spectral Regularization and Data Blending for the Track 2 of the CD-FSL challenge. We use different feature mapp...更多

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Zhao Zhen
Zhao Zhen
Liu Bingyu
Liu Bingyu
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