Yusuke Tanaka

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

This work proposes a novel meta-learning setting that avoids model inversion by jointly leveraging pre-trained models and unlabeled data and introduces a task-weighting mechanism based on task confidence and class distribution balance to ensure effective meta-learning.