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Meta-Exploiting Complementary Semantic Consistency for Cross-Domain Few-Shot Learning Promotion
Summary
This study introduces a new meta-learning framework to improve cross-domain few-shot learning (CD-FSL) by reducing simplicity bias. The novel approach uses complementary semantic consistency (CSC) to distill transferable features, enhancing model generalization across different domains.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computer Vision
Background:
- Meta-learning shows promise for cross-domain few-shot learning (CD-FSL).
- Existing methods suffer from simplicity bias, hindering generalization due to reliance on shortcut patterns.
- This bias leads to models that perform well on source domains but fail on target domains.
Purpose of the Study:
- To propose a novel meta-learning framework to alleviate simplicity bias in CD-FSL.
- To introduce a new inductive bias, complementary semantic consistency (CSC), for distilling cross-domain transferable features.
- To provide a theoretical foundation and practical implementations of the proposed framework.
Main Methods:
- Introduced complementary semantic consistency (CSC) by enforcing semantic consistency between complementary feature learning schemes.
- Developed a theoretical foundation demonstrating CSC's ability to establish tighter generalization bounds and learn domain-invariant features.
- Proposed a general meta-learning framework using parallel networks with different input forms and knowledge distillation losses.
Main Results:
- Empirical results on diverse benchmarks affirm the proposed framework's advantages in CD-FSL.
- Two effective meta-learners were instantiated, one using local-global image views and another using spatial-frequency decomposition.
- The framework successfully distilled cross-domain transferable features, leading to improved generalization performance.
Conclusions:
- The proposed meta-learning framework effectively mitigates simplicity bias in CD-FSL.
- Complementary semantic consistency (CSC) is a viable strategy for learning domain-invariant features.
- The framework offers a general and effective approach for enhancing generalization in few-shot learning tasks.
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