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FREE: Faster and Better Data-Free Meta-Learning
Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-trained models without requiring the original data, presenting practical benefits in contexts constrained by data privacy concerns.
Task-Distributionally Robust Data-Free Meta-Learning
TDS leads to a biased meta-learner because of the skewed task distribution towards newly generated tasks.
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