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In this paper, we present our proposed ensemble model with batch spectralregularization and data blending mechanisms for the Track 2 problem of thecross-domain few-shot learning (CD-FSL) challenge. We build a multi-branchensemble framework by using diverse feature transformation matrices, whiledeploying batch spectral feature regularization on each branch to improve themodel's transferability. Moreover, we propose a data blending method to exploitthe unlabeled data and augment the sparse support set in the target domain. Ourproposed model demonstrates effective performance on the CD-FSL benchmarktasks.

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