Due to the availability of only a few labeled instances for the novel target
prediction task and the significant domain shift between the well annotated
source domain and the target domain, cross-domain few-shot learning (CDFSL)
induces a very challenging adaptation problem. In this paper, we propose a
simple Adaptive Weighted Co-Learning (AWCoL) method to address the CDFSL
challenge by adapting two independently trained source prototypical
classification models to the target task in a weighted co-learning manner. The
proposed method deploys a weighted moving average prediction strategy to
generate probabilistic predictions from each model, and then conducts adaptive
co-learning by jointly fine-tuning the two models in an alternating manner
based on the pseudo-labels and instance weights produced from the predictions.
Moreover, a negative pseudo-labeling regularizer is further deployed to improve
the fine-tuning process by penalizing false predictions. Comprehensive
experiments are conducted on multiple benchmark datasets and the empirical
results demonstrate that the proposed method produces state-of-the-art CDFSL
performance.

通过自适应加权联合学习的方法，将两个独立训练的源典型分类模型适应目标任务，以解决交域少样本学习的挑战，该方法采用加权移动平均预测策略生成概率性预测，并基于从预测中产生的伪标签和实例权重交替地联合微调两个模型。此外，还通过惩罚错误预测部署了负伪标签正则化器来改进微调过程，综合实验结果在多个基准数据集上进行了验证，证明了该方法在交域少样本学习性能方面达到了最先进水平。