Combat Data Shift in Few-shot Learning with Knowledge Graph

• URL: http://arxiv.org/abs/2101.11354v1
• Date: Wed, 27 Jan 2021 12:35:18 GMT
• Title: Combat Data Shift in Few-shot Learning with Knowledge Graph
• Authors: Yongchun zhu, Fuzhen Zhuang, Xiangliang Zhang, Zhiyuan Qi, Zhiping Shi and Qing He
• Abstract summary: In real-world applications, few-shot learning paradigm often suffers from data shift. Most existing few-shot learning approaches are not designed with the consideration of data shift. We propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations.
• Score: 42.59886121530736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learning approaches achieve the expected performance in the scenario where all samples are drawn from the same distributions (i.i.d. observations). However, in real-world applications, few-shot learning paradigm often suffers from data shift, i.e., samples in different tasks, even in the same task, could be drawn from various data distributions. Most existing few-shot learning approaches are not designed with the consideration of data shift, and thus show downgraded performance when data distribution shifts. However, it is non-trivial to address the data shift problem in few-shot learning, due to the limited number of labeled samples in each task. Targeting at addressing this problem, we propose a novel metric-based meta-learning framework to extract task-specific representations and task-shared representations with the help of knowledge graph. The data shift within/between tasks can thus be combated by the combination of task-shared and task-specific representations. The proposed model is evaluated on popular benchmarks and two constructed new challenging datasets. The evaluation results demonstrate its remarkable performance.

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