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


Abdullah Alchihabi (Carleton University), Marzi Heidari (Carleton University), Yuhong Guo (Carleton University)
The 35th British Machine Vision Conference
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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.

Citation

@inproceedings{Alchihabi_2024_BMVC,
author    = {Abdullah Alchihabi and Marzi Heidari and Yuhong Guo},
title     = {Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0986.pdf}
}


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