Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning
Abdullah Alchihabi Marzi Heidari Yuhong Guo
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.