Meta-Task: A Method-Agnostic Framework for Learning to Regularize in Few-Shot Learning

2024-02-27

AI Summary

To address overfitting and poor generalization in few-shot learning (FSL) caused by data scarcity, this paper proposes a model-agnostic meta-regularization framework. The method constructs labeled and unlabeled auxiliary meta-tasks to jointly enforce self-supervised image reconstruction and embedding-space regularization, thereby imposing generic constraints on the model’s latent space. A lightweight Task-Decoder enables latent-space reconstruction without task-specific architectural design. Evaluated on Mini-ImageNet, Tiered-ImageNet, and FC100, our approach consistently outperforms state-of-the-art methods—achieving faster convergence, lower generalization error, and reduced result variance—without requiring intricate hyperparameter tuning. The core contribution lies in the first unified integration of task-agnostic meta-regularization with latent-space reconstruction within the few-shot meta-learning paradigm, significantly enhancing model robustness and generalization capability.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Learning to Search
• Computer Vision: Representation Learning for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Overfitting is a significant challenge in Few-Shot Learning (FSL), where models trained on small, variable datasets tend to memorize rather than generalize to unseen tasks. Regularization is crucial in FSL to prevent overfitting and enhance generalization performance. To address this issue, we introduce Meta-Task, a novel, method-agnostic framework that leverages both labeled and unlabeled data to enhance generalization through auxiliary tasks for regularization. Specifically, Meta-Task introduces a Task-Decoder, which is a simple example of the broader framework that refines hidden representations by reconstructing input images from embeddings, effectively mitigating overfitting. Our framework's method-agnostic design ensures its broad applicability across various FSL settings. We validate Meta-Task's effectiveness on standard benchmarks, including Mini-ImageNet, Tiered-ImageNet, and FC100, where it consistently improves existing state-of-the-art meta-learning techniques, demonstrating superior performance, faster convergence, reduced generalization error, and lower variance-all without extensive hyperparameter tuning. These results underline Meta-Task's practical applicability and efficiency in real-world, resource-constrained scenarios.

Problem

• Overfitting in Few-Shot Learning
• Enhancing generalization in FSL
• Method-agnostic regularization framework

Innovation

• Method-agnostic regularization framework
• Utilizes labeled and unlabeled data
• Task-Decoder refines hidden representations