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

Zhen Zhao; Bingyu Liu; Yuhong Guo; Jieping Ye

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arXiv · 2006.04323

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

Zhen Zhao·Bingyu Liu·Yuhong Guo·Jieping Ye

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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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Zhen Zhao, Bingyu Liu, Yuhong Guo, Jieping Ye. 2020-06-08. Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data. https://arxiv.org/abs/2006.04323

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