Labeled-Data-Free Meta-Learning: Efficient Task Generation Using Pre-trained Models and Unlabeled Data

2026-07-02

AI Summary

This work addresses the challenge of data-scarce real-world scenarios by proposing an efficient label-free meta-learning framework that eliminates the need for computationally expensive model inversion used in existing data-free approaches. Instead, the method leverages a pre-trained model to generate soft labels for unlabeled data, constructing meta-tasks without synthetic data generation. To enhance meta-training efficacy, it introduces a task-weighting mechanism based on task confidence and class distribution balance. The proposed approach achieves substantial improvements in both efficiency and performance, yielding accuracy gains of 8.4%–36.4% on few-shot classification benchmarks while accelerating computation by up to 104× compared to prior methods.

Abstract

Meta-learning without labeled data is crucial for real-world applications, where obtaining labeled datasets can be expensive or restricted due to privacy concerns. Data-Free Meta-Learning (DFML) addresses this challenge by leveraging pre-trained models without access to training data. However, existing DFML methods rely on model inversion to generate training data, a process that is generally difficult and computationally expensive due to the need to generate high-dimensional data matching the original distribution. To address this limitation, we propose a novel meta-learning setting that avoids model inversion by jointly leveraging pre-trained models and unlabeled data. Our method generates meta-training tasks by assigning soft labels from pre-trained models to unlabeled data. Since the quality of these tasks can vary, we introduce a task-weighting mechanism based on task confidence and class distribution balance to ensure effective meta-learning. Extensive experiments demonstrate that our approach substantially reduces computational cost and improves generalization, achieving up to 104-fold speedup and 8.4 percent to 36.4 percent improvements in few-shot classification accuracy compared to state-of-the-art DFML methods.

Problem

• meta-learning
• labeled-data-free
• data-free
• unlabeled data
• model inversion

Innovation

• data-free meta-learning
• pre-trained models
• unlabeled data
• soft labels
• task weighting