Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning

• URL: http://arxiv.org/abs/2312.03928v1
• Date: Wed, 6 Dec 2023 22:09:52 GMT
• Title: Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning
• Authors: Abdullah Alchihabi, Marzi Heidari, Yuhong Guo
• Abstract summary: Cross-domain few-shot learning (CDFSL) induces a very challenging adaptation problem. We propose a simple Adaptive Weighted Co-Learning (AWCoL) method to address the CDFSL challenge. Comprehensive experiments are conducted on multiple benchmark datasets and the empirical results demonstrate that the proposed method produces state-of-the-art CDFSL performance.
• Score: 23.615250207134004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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