  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.
