  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.
