Abstract
Metric-based few-shot learning (FSL) methods have been attracting more and more research attention since they reflect a simpler and more effective inductive bias in the limited-data regime. The episodic evaluating method is widely used in the metric-based FSL methods, and the task-wise relative metric is critical to improving the performance of the episodic method. However, the commonly used metrics in existing metric-based FSL methods typically measure the absolute distance in a smooth and uniform feature space. Observing this, this paper proposed mapping the features into the task-specific sub-space by designing the correlation matrix of task-specific prototypical vectors, which induces a task-specific method-agnostic (TSMA) metric. The TSMA can be viewed as an adaptive linear classifier and hence is method-agnostic. In addition, the TSMA is manually designed and thus is parameter-free. The extensive experiments evaluated on various datasets show that TSMA outperformed the SOTA methods by 1.5–4.4%. And the ablation study shows that TSMA could adaptively adjust the scale of the similarity items and the scaling items, allowing for the models to easily optimized.
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For example, the original test dataset contains 100 classes, and in each 5-way-1-shot task, the 5-classification is conducted, and the labels are set to \(\{1, \ldots , 5\}\)
This dataset can be downloaded from http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.html..
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This work was supported by the National Natural Science Foundation of China (No. 62071060) and the Beijing Key Laboratory of Work Safety and Intelligent Monitoring Foundation.
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Wang, H., Li, Y. Task-specific method-agnostic metric for few-shot learning. Neural Comput & Applic 35, 3115–3124 (2023). https://doi.org/10.1007/s00521-022-07858-2
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DOI: https://doi.org/10.1007/s00521-022-07858-2