[2311.14756] Task-Distributionally Robust Data-Free Meta-Learning

Computer Science > Machine Learning arXiv:2311.14756 (cs) [Submitted on 23 Nov 2023 (v1), last revised 10 Apr 2026 (this version, v2)] Title:Task-Distributionally Robust Data-Free Meta-Learning Authors:Zixuan Hu, Yongxian Wei, Li Shen, Zhenyi Wang, Baoyuan Wu, Chun Yuan, Dacheng Tao View a PDF of the paper titled Task-Distributionally Robust Data-Free Meta-Learning, by Zixuan Hu and 6 other authors View PDF HTML (experimental) Abstract:Data-Free Meta-Learning (DFML) aims to enable efficient learning of unseen few-shot tasks, by meta-learning from multiple pre-trained models without accessing their original training data. While existing DFML methods typically generate synthetic data from these models to perform meta-learning, a comprehensive analysis of DFML's robustness-particularly its failure modes and vulnerability to potential attacks-remains notably absent. Such an analysis is crucial as algorithms often operate in complex and uncertain real-world environments. This paper fills this significant gap by systematically investigating the robustness of DFML, identifying two critical but previously overlooked vulnerabilities: Task-Distribution Shift (TDS) and Task-Distribution Corruption (TDC). TDS refers to the sequential shifts in the evolving task distribution, leading to the catastrophic forgetting of previously learned meta-knowledge. TDC exposes a security flaw of DFML, revealing its susceptibility to attacks when the pre-trained model pool includes untrustworthy mode...