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

• URL: http://arxiv.org/abs/2006.04323v2
• Date: Tue, 9 Jun 2020 07:52:51 GMT
• Title: Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
• Authors: Zhen Zhao, Bingyu Liu, Yuhong Guo, Jieping Ye
• Abstract summary: We build a multi-branch ensemble framework by using diverse feature transformation matrices. We propose a data blending method to exploit the unlabeled data and augment the sparse support set in the target domain.
• Score: 75.94147344921355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge. We build a multi-branch ensemble framework by using diverse feature transformation matrices, while deploying batch spectral feature regularization on each branch to improve the model's transferability. Moreover, we propose a data blending method to exploit the unlabeled data and augment the sparse support set in the target domain. Our proposed model demonstrates effective performance on the CD-FSL benchmark tasks.

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