Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification

• URL: http://arxiv.org/abs/2005.08463v3
• Date: Thu, 21 May 2020 02:44:03 GMT
• Title: Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
• Authors: Bingyu Liu, Zhen Zhao, Zhenpeng Li, Jianan Jiang, Yuhong Guo, Jieping Ye
• Abstract summary: We propose an ensemble prediction model by performing diverse feature transformations after a feature extraction network. We use a batch spectral regularization term to suppress the singular values of the feature matrix during pre-training to improve the generalization ability of the model. The proposed model can then be fine tuned in the target domain to address few-shot classification.
• Score: 66.91839845347604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge. Specifically, we proposes to construct an ensemble prediction model by performing diverse feature transformations after a feature extraction network. On each branch prediction network of the model we use a batch spectral regularization term to suppress the singular values of the feature matrix during pre-training to improve the generalization ability of the model. The proposed model can then be fine tuned in the target domain to address few-shot classification. We also further apply label propagation, entropy minimization and data augmentation to mitigate the shortage of labeled data in target domains. Experiments are conducted on a number of CD-FSL benchmark tasks with four target domains and the results demonstrate the superiority of our proposed model.

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