Toward Better Generalization in Few-Shot Learning through the Meta-Component Combination

2025-11-07

AI Summary

In few-shot learning, existing metric-based meta-learning approaches suffer from degraded generalization to unseen classes due to over-reliance on deep metrics optimized for seen classes. To address this, we propose a meta-component composition framework that models classifiers as reconfigurable sets of meta-components. During meta-training, orthogonal regularization explicitly decouples these components, enhancing their diversity and functional specificity—thereby enabling effective extraction of task-invariant discriminative substructures. This decoupling mitigates overfitting to seen classes and improves cross-class generalization. Evaluated on standard benchmarks including Mini-ImageNet and Tiered-ImageNet, our method achieves significant improvements over state-of-the-art metric-learning approaches. Empirical results validate the efficacy of both meta-component decoupling and compositional modeling for robust few-shot classification.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

In few-shot learning, classifiers are expected to generalize to unseen classes given only a small number of instances of each new class. One of the popular solutions to few-shot learning is metric-based meta-learning. However, it highly depends on the deep metric learned on seen classes, which may overfit to seen classes and fail to generalize well on unseen classes. To improve the generalization, we explore the substructures of classifiers and propose a novel meta-learning algorithm to learn each classifier as a combination of meta-components. Meta-components are learned across meta-learning episodes on seen classes and disentangled by imposing an orthogonal regularizer to promote its diversity and capture various shared substructures among different classifiers. Extensive experiments on few-shot benchmark tasks show superior performances of the proposed method.

Problem

• Addresses overfitting in few-shot learning by enhancing classifier generalization
• Proposes meta-component combination to capture diverse shared substructures across classifiers
• Improves generalization on unseen classes through orthogonal regularization of meta-components

Innovation

• Learns classifiers as meta-component combinations
• Imposes orthogonal regularizer for diversity
• Captures shared substructures across classifiers